Tesis: "Personalization"

1

Elbassuoni, Shady. "Adaptive personalization of web search : task sensitive approach to search personalization /". Saarbrücken : VDM Verlag Dr. Müller, 2008. http://d-nb.info/988664186/04.

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Almerfors, Mattias. "Visualization of Personalization Information". Thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97829.

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There has been an increasing use of Internet as an information source and also as a new channel for business-to-business and business-to-consumer communication. This has lead to an increasing need for understanding web traffic and especially a growing need for analyzing this traffic, which demands new analysis tools. The objective for this study was to create an application framework for visualizing XML-formatted data representing web traffic reports. Visualization in two dimensions is created by using Batik, a Java ™ toolkit with the purpose of transforming a well formed XML document into a SVG document and then transcode this into an image format supported by today’s web browsers. Visualization in three dimensions is created by using the Java3D API. In order to create an aesthetically well-balanced graph in three dimensions representing a model of the web traffic on a site, the framework uses a layout algorithm called Graphical Embedder (GEM). This is a force-directed algorithm which uses physical analogy to model the graph. GEM emphasises the spring force approach combined with simulated annealing and the detection of oscillations and rotations. The design of the framework is built up using J2EE techniques. At the front-end this application framework uses JSP and Applet showing the graph for a customer using this service. At the back-end the framework uses Servlets combined with a Filter.

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3

Asif, Muhammad. "Personalization of Mobile Services". Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25576.

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The mobile era is well established and the number of smartphone users is showing exponential growth. The capability of smartphones and enabling technologies is also increasing and has opened many possibilities of personalized mobile services. The goal of personalization is to support the user by providing the right service at the rightmoment. Early focus of personalization was on content adaptations in different information systems. The new approaches of personalization are still needed for mobileservices as it is a compelling feature of mobile communication systems for both endusers and service providers.Personalization is providing a means of fulfilling users’ needs more effectively andefficiently and, consequently increasing users’ satisfaction. By providing successfulpersonalization, a high degree of user satisfaction and a pleasant user experience can beachieved. Some features of personalization can cause problems and may outweigh thebenefits of personalization.This thesis has focused on how to achieve scrutable mobile client-side personalizationwhile keeping the user’s privacy. The issue of privacy in personalization of mobileservices can be reduced by shifting the control of their personal information towards theusers. Our research goal is to understand and improve the personalization process anddevelop an architecture for scrutable mobile client-side personalization while keepingthe user s’ privacy. Moreover, there is a need to develop an evaluation framework tomeasure the effectiveness of mobile services personalization. A design science researchmethodology is adopted in this research work. More particular contributions of thethesis are as follows: C1: Identifications of the research issues and challenges in personalization of mobileservices. C2: An approach for delivering personalized mobile services. C3: Development of mobile client-side personalization architecture. C4: Development of mobile services Personalization Evaluation Model. C5: Identification of the prospects of scrutable personalization of mobile services.

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4

Donnelly, Christopher. "Enhancing Personalization Within ASSISTments". Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/249.

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ASSISTments is an online adaptive tutoring system with the ability to provide assistance to students in the form of hints and scaffolding. ASSISTments has many features to help students improve their knowledge. Researchers run studies in order to discover ways for students to learn better but ASSISTments is missing one major aspect for researchers: student level personalization. It is easy to create an assignment for a particular class or school but it would take much longer to create an assignment for each student and it would be difficult for the teacher to look through many assignment reports. One of the strongest code blocks in coding is the if-then; allowing the program to branch off to another set of code under certain circumstances. ASSISTments needed an if-then system in order for students to branch off to other parts of the assignment under certain circumstances. With this, researchers would be able to personalize assignments to give more help to lower knowledge students or allow students to get a choice of what kind of tutoring they would like to receive. With this idea in mind, the basic if-then structure was implemented into ASSISTments using problem or problem set correctness as the condition statement. Once the if-then system was created opportunities opened to create additional experiments and run studies in ASSISTments. The basic if-then was limited in using correctness only for its condition statement. This meant that a new if-then system would need to be implemented to include custom condition statements that allowed the researcher to have the assignment branch on any condition using all the information recorded in the assignment. While work was being done on the if-then system, research was being done and two papers were written on partial credit in ASSISTments. Partial credit was found out to be as accurate as knowledge tracing in determining student performance on the next problem. Once a partial credit algorithm was found, a study using if-then was analyzed. It was found that there was no statistically significant difference between students who were given a choice on their feedback and students who received no choice.

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5

Yang, Yanwu. "Towards spatial web personalization". Paris, ENSAM, 2006. https://pastel.archives-ouvertes.fr/pastel-00002481.

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La mise à disposition d'information et de services spatiaux a récemment proliféré sur le web dans la mesure où la plupart de nos activités quotidiennes sont géo-réferencées. Les communautés d'utilisateurs de services spatiaux sur le web sont de plus larges et variées, en constante expansion et transformation avec une augmentation constante des gammes d'applications proposées. Cette profusion d'applications entraîne un nombre important de problématiques de recherche, et notamment celles liées à l'identification des intérêts et des préférences de l'utilisateur, afin d'adapter les services délivrés aux besoins du client. Cette recherche propose une architecture intégrée de modélisation de profils d'utilisateur et d'approximation de leurs préférences, et de mise à disposition de services personnalisés orientés vers l'information spatiale. L'architecture proposée se compose d'un service de personnalisation et d'un modèle sémantique orienté utilisateur. Ces deux composants communiquent des informations sur l'utilisateur par des processus interactifs. Ce service de personalisation est basé sur trois principes : la mémoire associative neurale bi-directionnelle, des mesures contextuelles et spatiales orientées-utilisateur de proximité et de similarité, des schémas d'image et des concepts d'affordance. Ces concepts sont implémentés à partir d'une interface utilisateur qui intègre les différents composants identifiés, et offre un éventail de stratégies personnalisées de recherche, et un moteur hybride de personnalisation. Le modèle d'utilisateur utilise des logiques expressives de description pour caractériser les différentes catégories d'utilisateur, afin d'adapter les besoins d'utilisateur aux exigences d'une application. Un scénario dans le domaine du tourisme et un prototype Java réalisent une validation expérimentale de notre recherche à partir de techniques de personnalisation
In the past few years, spatial information and services have proliferated on the Web, due to the fact that most of our daily activities are related to the spatial dimension. The user communities involved in spatial web services are essentially diverse, still in an expansion and transformation with constantly increasing number of user and applications. This opens many research challenges, such as the elicitation of user's interests and preferences and customization of information services on the spatial Web. This PhD research proposes an integrated framework for user modeling and preference elicitation, and personalization services on the spatial Web. The framework identifies personalization services and a semantic user model for spatial web applications. These two components communicate information and knowledge about the user through inter-process communications. The personalization services are based on three mechanisms: the Bi-directional Neural Associative Memory, user-centric spatial proximity and similarity measures, image schemata and affordance concepts. A web-based user interface is integrated with these components, and offers a spectrum of personalized search strategies and a hybrid personalization engine. The user model employs expressive description logics to describe assumptions about the user and to infer implicit user features from user's descriptions as required by an application system. An application scenario in the tourism domain and a Web-based Java prototype provide an experimental validation of the research framework and identified personalization techniques

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6

Boutet, Antoine. "Decentralizing news personalization systems". Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S023/document.

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L'évolution rapide du web a changé la façon dont l'information est créée, distribuée, évaluée et consommée. L'utilisateur est dorénavant mis au centre du web en devenant le générateur de contenu le plus prolifique. Pour évoluer dans le flot d'informations, les utilisateurs ont besoin de filtrer le contenu en fonction de leurs centres d'intérêts. Pour bénéficier de contenus personnalisés, les utilisateurs font appel aux réseaux sociaux ou aux systèmes de recommandations exploitant leurs informations privées. Cependant, ces systèmes posent des problèmes de passage à l'échelle, ne prennent pas en compte la nature dynamique de l'information et soulèvent de multiples questions d'un point de vue de la vie privée. Dans cette thèse, nous exploitons les architectures pair-à-pair pour implémenter des systèmes de recommandations pour la dissémination personnalisée des news. Une approche pair-à-pair permet un passage à l'échelle naturel et évite qu'une entité centrale contrôle tous les profils des utilisateurs. Cependant, l'absence de connaissance globale fait appel à des schémas de filtrage collaboratif qui doivent palier les informations partielles et dynamiques des utilisateurs. De plus, ce schéma de filtrage doit pouvoir respecter la vie privée des utilisateurs. La première contribution de cette thèse démontre la faisabilité d'un système de recommandation de news totalement distribué. Le système proposé maintient dynamiquement un réseau social implicit pour chaque utilisateur basé sur les opinions qu'il exprime à propos des news reçues. Les news sont disséminées au travers d'un protocole épidémique hétérogène qui (1) biaise l'orientation des cibles et (2) amplifie la dissémination de chaque news en fonction du niveau d'intérêt qu'elle suscite. Ensuite, pour améliorer la vie privée des utilisateurs, nous proposons des mécanismes d'offuscation permettant de cacher le profil exact des utilisateurs sans trop dégrader la qualité de la recommandation fournie. Enfin, nous explorons un nouveau modèle tirant parti des avantages des systèmes distribués tout en conservant une architecture centralisée. Cette solution hybride et générique permet de démocratiser les systèmes de recommandations en offrant aux fournisseurs de contenu un système de personnalisation à faible coût
The rapid evolution of the web has changed the way information is created, distributed, evaluated and consumed. Users are now at the center of the web and becoming the most prolific content generators. To effectively navigate through the stream of available news, users require tools to efficiently filter the content according to their interests. To receive personalized content, users exploit social networks and recommendation systems using their private data. However, these systems face scalability issues, have difficulties in coping with interest dynamics, and raise a multitude of privacy challenges. In this thesis, we exploit peer-to-peer networks to propose a recommendation system to disseminate news in a personalized manner. Peer-to-peer approaches provide highly-scalable systems and are an interesting alternative to Big brother type companies. However, the absence of any global knowledge calls for collaborative filtering schemes that can cope with partial and dynamic interest profiles. Furthermore, the collaborative filtering schemes must not hurt the privacy of users. The first contribution of this thesis conveys the feasibility of a fully decentralized news recommender. The proposed system constructs an implicit social network based on user profiles that express the opinions of users about the news items they receive. News items are disseminated through a heterogeneous gossip protocol that (1) biases the orientation of the dissemination, and (2) amplifies dissemination based on the level of interest in each news item. Then, we propose obfuscation mechanisms to preserve privacy without sacrificing the quality of the recommendation. Finally, we explore a novel scheme leveraging the power of the distribution in a centralized architecture. This hybrid and generic scheme democratizes personalized systems by providing an online, cost-effective and scalable architecture for content providers at a minimal investment cost

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7

Hoang, Van Tieng. "Measuring Web Search Personalization". Thesis, IMT Alti Studi Lucca, 2018. http://e-theses.imtlucca.it/246/1/Hoang_phdthesis.pdf.

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Personalization in online services is the practice of tailoring data contents for customers according to what is supposed to be their expectation. By design, personalization has been considered an important tool to help users to find the most interesting relevant data. By doing that, personalization also filters the web contents and potentially narrows the view of users. In this context, our work aims to measure personalization levels of web search results. First, we study personalization for web search engines with two case studies about Google Search. The results show a remarkable level of Google personalized search results based on sets of keywords, and we show that a specific website appears as prevalent in the results of web searches. Second, we measure the personalization degrees of an online news aggregator to provide a wider view of the problems. It also shows that compelling evidence such as “suggested for you" heavily depends on past users’ activities. Finally, we study personalization of search results on an online shopping platformby measuring price steering phenomenon. Particularly, we investigate the impacts of online behaviours, locations and economic performance factors, and we observe that price steering is based on user’s behaviours but it is also influenced by the geographic location of users.

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8

SONG, Songbo. "Advanced personalization of IPTV services". Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00814620.

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Internet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users' experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users' devices 'screen types, size, supported resolution, '' and networks available network types to be used by the user, available bandwidth, ..') as well as the context of the service itself (content type and description, available format 'HD/SD', available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users' identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user's environment (devices and networks status), interpreting user's requirements and making the user's interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users' preferences) which in turn highly improves the users' Quality of Experience (QoE) and enriching the offers of IPTV services

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9

Song, Xiang Ph D. Massachusetts Institute of Technology. "Personalization of future urban mobility". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120637.

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Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 91-97).
In the past few years, we have been experiencing rapid growth of new mobility solutions fueled by a myriad of innovations in technologies such as automated vehicles and in business models such as shared-ride services. The emerging mobility solutions are often required to be profitable, sustainable, and efficient while serving heterogeneous needs of mobility consumers. Given high-resolution consumer mobility behavior collected from smartphones and other GPS-enabled devices, the operational management strategies for future urban mobility can be personalized and serve for various system objectives. This thesis focuses on the personalization of future urban mobility through the personalized menu optimization model. The model built upon individual consumer's choice behavior generates a personalized menu for app-based mobility solutions. It integrates behavioral modeling of consumer mobility choice with optimization objectives. Individual choice behavior is modeled through logit mixture and the parameters are estimated with a hierarchical Bayes (HB) procedure. In this thesis, we first present an enhancement to HB procedure with alternative priors for covariance matrix estimation in order to improve the estimation performance. We also evaluate the benefits of personalization through a Boston case study based on real travel survey data. In addition, we present a sequential personalized menu optimization algorithm that addresses trade-off between exploration (learn uncertain demand of menus) and exploitation (offer the best menu based on current knowledge). We illustrate the benefits of exploration under different conditions including different types of heterogeneity.
by Xiang Song.
Ph. D. in Transportation

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10

Li, Andrew A. (Andrew Andi). "Algorithms for large-scale personalization". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119351.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 191-205).
The term personalization typically refers to the activity of online recommender systems, and while product and content personalization is now ubiquitous in e-commerce, systems today remain relatively primitive: they are built on a small fraction of available data, run with heuristic algorithms, and restricted to e-commerce applications. This thesis addresses key challenges and new applications for modern, large-scale personalization. In particular, this thesis is outlined as follows: First, we formulate a generic, flexible framework for learning from matrix-valued data, including the kinds of data commonly collected in e-commerce. Underlying this framework is a classic de-noising problem called tensor recovery, for which we provide an efficient algorithm, called Slice Learning, that is practical for massive datasets. Further, we establish near-optimal recovery guarantees that represent an order improvement over the best available results for this problem. Experimental results from a music recommendation platform are shown. Second, we apply this de-noising framework to new applications in precision medicine where data are routinely complex and in high dimensions. We describe a simple, accurate proteomic blood test (a 'liquid biopsy') for cancer detection that relies on de-noising via the Slice Learning algorithm. Experiments on plasma from healthy patients that were later diagnosed with cancer demonstrate that our test achieves diagnostically significant sensitivities and specificities for many types of cancers in their earliest stages. Third, we present an efficient, principled approach to operationalizing recommendations, i.e. the decision of exactly what items to recommend. Motivated by settings such as online advertising where the space of items is massive and recommendations must be made in milliseconds, we propose an algorithm that simultaneously achieves two important properties: (1) sublinear runtime and (2) a constant-factor guarantee under a wide class of choice models. Our algorithm relies on a new sublinear time sampling scheme, which we develop to solve a class of problems that subsumes the classic nearest neighbor problem. Results from a massive online content recommendation firm are given. Fourth, we address the problem of cost-effectively executing a broad class of computations on commercial cloud computing platforms, including the computations typically done in personalization. We formulate this as a resource allocation problem and introduce a new approach to modeling uncertainty - the Data-Driven Prophet Model - that treads the line between stochastic and adversarial modeling, and is amenable to the common situation where stochastic modeling is challenging, despite the availability of copious historical data. We propose a simple, scalable algorithm that is shown to be order-optimal in this setting. Results from experiments on a commercial cloud platform are shown.
by Andrew A. Li.
Ph. D.

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11

Perugini, Saverio. "Program Transformations for Information Personalization". Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11196.

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Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation. The central thesis of this dissertation is that modeling interaction explicitly in a representation, and studying how partial information can be harnessed in it by program transformations to direct the flow of the interaction, can provide insight into, reveal opportunities for, and define a model for personalized interaction. To evaluate this thesis, a formal modeling methodology is developed for personalizing interactions with information systems, especially hierarchical hypermedia, based on program transformations. The predominant form of personalized interaction developed in this thesis is out-of-turn interaction, a technique which empowers the user to take the initiative in a user--system dialog by providing unsolicited, but relevant, information out-of-turn. Out-of-turn interaction helps flexibly bridge any mismatch between the user's model of information seeking and the system's hardwired hyperlink structure in a manner fundamentally different from extant solutions, such as multiple faceted browsing classifications and search tools. This capability is showcased through two interaction interfaces using alternate modalities to capture and communicate out-of-turn information to the underlying system: a toolbar embedded into a traditional browser for out-of-turn textual input and voice-enabled content pages for out-of-turn speech input. The specific research issues addressed involve identifying and developing representations and transformations suitable for general classes of hierarchical hypermedia, providing supplemental interactions for improving the personalized experience, and studying user's (out-of-turn) interactions with resulting systems.
Ph. D.

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12

Song, Songbo. "Advanced personalization of IPTV services". Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0001/document.

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Le monde de la TV est en cours de transformation de la télévision analogique à la télévision numérique, qui est capable de diffuser du contenu de haute qualité, offrir aux consommateurs davantage de choix, et rendre l'expérience de visualisation plus interactive. IPTV (Internet Protocol TV) présente une révolution dans la télévision numérique dans lequel les services de télévision numérique sont fournis aux utilisateurs en utilisant le protocole Internet (IP) au dessus d’une connexion haut débit. Les progrès de la technologie IPTV permettra donc un nouveau modèle de fourniture de services. Les fonctions offertes aux utilisateurs leur permettent de plus en plus d’autonomie et de plus en plus de choix. Il en est notamment ainsi de services de type ‘nTS’ (pour ‘network Time Shifting’ en anglais) qui permettent à un utilisateur de visionner un programme de télévision en décalage par rapport à sa programmation de diffusion, ou encore des services de type ‘nPVR’ (pour ‘network Personal Video Recorder’ en anglais) qui permettent d’enregistrer au niveau du réseau un contenu numérique pour un utilisateur. D'autre part, l'architecture IMS proposée dans NGN fournit une architecture commune pour les services IPTV. Malgré les progrès rapides de la technologie de télévision interactive (comprenant notamment les technologies IPTV et NGN), la personnalisation de services IPTV en est encore à ses débuts. De nos jours, la personnalisation des services IPTV se limite principalement à la recommandation de contenus et à la publicité ciblée. Ces services ne sont donc pas complètement centrés sur l’utilisateur, alors que choisir manuellement les canaux de diffusion et les publicités désirées peut représenter une gêne pour l’utilisateur. L’adaptation des contenus numériques en fonction de la capacité des réseaux et des dispositifs utilisés n’est pas encore prise en compte dans les implémentations actuelles. Avec le développement des technologies numériques, les utilisateurs sont amenés à regarder la télévision non seulement sur des postes de télévision, mais également sur des smart phones, des tablettes digitales, ou encore des PCs. En conséquence, personnaliser les contenus IPTV en fonction de l’appareil utilisé pour regarder la télévision, en fonction des capacités du réseau et du contexte de l’utilisateur représente un défi important. Cette thèse présente des solutions visant à améliorer la personnalisation de services IPTV à partir de trois aspects: 1) Nouvelle identification et authentification pour services IPTV. 2) Nouvelle architecture IPTV intégrée et comportant un système de sensibilité au contexte pour le service de personnalisation. 3) Nouveau service de recommandation de contenu en fonction des préférences de l’utilisateur et aussi des informations contextes
Internet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users’ experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users’ devices ‘screen types, size, supported resolution, ‘‘ and networks available network types to be used by the user, available bandwidth, ..’) as well as the context of the service itself (content type and description, available format ‘HD/SD’, available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users’ identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user’s environment (devices and networks status), interpreting user’s requirements and making the user’s interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users’ preferences) which in turn highly improves the users’ Quality of Experience (QoE) and enriching the offers of IPTV services

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13

Song, Songbo. "Advanced personalization of IPTV services". Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0001.

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Le monde de la TV est en cours de transformation de la télévision analogique à la télévision numérique, qui est capable de diffuser du contenu de haute qualité, offrir aux consommateurs davantage de choix, et rendre l'expérience de visualisation plus interactive. IPTV (Internet Protocol TV) présente une révolution dans la télévision numérique dans lequel les services de télévision numérique sont fournis aux utilisateurs en utilisant le protocole Internet (IP) au dessus d’une connexion haut débit. Les progrès de la technologie IPTV permettra donc un nouveau modèle de fourniture de services. Les fonctions offertes aux utilisateurs leur permettent de plus en plus d’autonomie et de plus en plus de choix. Il en est notamment ainsi de services de type ‘nTS’ (pour ‘network Time Shifting’ en anglais) qui permettent à un utilisateur de visionner un programme de télévision en décalage par rapport à sa programmation de diffusion, ou encore des services de type ‘nPVR’ (pour ‘network Personal Video Recorder’ en anglais) qui permettent d’enregistrer au niveau du réseau un contenu numérique pour un utilisateur. D'autre part, l'architecture IMS proposée dans NGN fournit une architecture commune pour les services IPTV. Malgré les progrès rapides de la technologie de télévision interactive (comprenant notamment les technologies IPTV et NGN), la personnalisation de services IPTV en est encore à ses débuts. De nos jours, la personnalisation des services IPTV se limite principalement à la recommandation de contenus et à la publicité ciblée. Ces services ne sont donc pas complètement centrés sur l’utilisateur, alors que choisir manuellement les canaux de diffusion et les publicités désirées peut représenter une gêne pour l’utilisateur. L’adaptation des contenus numériques en fonction de la capacité des réseaux et des dispositifs utilisés n’est pas encore prise en compte dans les implémentations actuelles. Avec le développement des technologies numériques, les utilisateurs sont amenés à regarder la télévision non seulement sur des postes de télévision, mais également sur des smart phones, des tablettes digitales, ou encore des PCs. En conséquence, personnaliser les contenus IPTV en fonction de l’appareil utilisé pour regarder la télévision, en fonction des capacités du réseau et du contexte de l’utilisateur représente un défi important. Cette thèse présente des solutions visant à améliorer la personnalisation de services IPTV à partir de trois aspects: 1) Nouvelle identification et authentification pour services IPTV. 2) Nouvelle architecture IPTV intégrée et comportant un système de sensibilité au contexte pour le service de personnalisation. 3) Nouveau service de recommandation de contenu en fonction des préférences de l’utilisateur et aussi des informations contextes
Internet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users’ experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users’ devices ‘screen types, size, supported resolution, ‘‘ and networks available network types to be used by the user, available bandwidth, ..’) as well as the context of the service itself (content type and description, available format ‘HD/SD’, available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users’ identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user’s environment (devices and networks status), interpreting user’s requirements and making the user’s interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users’ preferences) which in turn highly improves the users’ Quality of Experience (QoE) and enriching the offers of IPTV services

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14

Schilke, Steffen Walter. "Multi-dimensional-personalization in mobile contexts". Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1576.

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During the dot com era the word 'personalisation' was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range/short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user's privacy so that an approach supporting anonymous recommendations through an extended 'Chinese Wall' will be described.

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15

Atwi, Aliaa. "Exploration vs. exploitation in coupon personalization". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115729.

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Thesis: Elec. E. in Computer Science, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 51-52).
Personalized offers aim to maximize profit by taking into account customer preferences inferred from past purchase behavior. For large retailers with extensive product offerings, learning customer preferences can be challenging due to relatively short purchase histories of most customers. To alleviate the dearth of data, we propose exploiting similarities among products and among customers to reduce problem dimensions. We also propose that retailers use personalized offers not only to maximize expected profit, but to actively learn their customers' preferences. An offer that does not maximize expected profit given current information may still provide valuable insights about customer preferences. This information enables more profitable coupon allocation and higher profits in the long run. In this thesis we 1) derive approximate inference algorithms to learn customer preferences from purchase data in real time, 2) formulate the retailers' offer allocation problem as a multi armed bandit and explore solution strategies.
by Aliaa Atwi.
Elec. E. in Computer Science

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16

Violante, João G. "Behavior-based personalization : strategies and Implications". Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65820.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 53-55).
The personalization of services and products offered to customers is becoming crucial for the success of companies. Firms that can maintain a personalized relationship with their customers will not only gain an advantage from competitors but will also benefit from having more loyal and valuable customers. The recent advances in technology and the associated cost reduction are allowing companies to gather information about their customers and their behavior in an easy and inexpensive way. This collection and analysis of behavior-based information increases the companies' knowledge about their customers and allows a more personalized approach. This thesis studies what has been accomplished in the domain of behavior-based personalization and in more detail what are the techniques and strategies being used and how companies can take advantage of its applications. Moreover, this thesis discusses the critical role of personalization in building effective customer relationships management (CRM) strategies.
by João G. Violante.
S.M.

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17

Sah, Melike. "Semantic linking and personalization in context". Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/66605/.

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The World Wide Web (WWW) is intended for humans to create and share documents. However, it does not support machine-processable data and automated processing. The Semantic Web is an extension to the WWW and can overcome its shortcomings. The Semantic Web provides the technology for creating and sharing data in machine-processable semantics. As a result, the data can be used and shared in effective ways between cross applications. In this thesis, we investigate the Semantic Web technologies for context-based hyperlink creation and personalization. Two different contributions are presented using Semantic Web technologies. First, we introduce and implement a novel personalized Semantic Web-enabled portal (known as a semantic portal), which is called SEMPort with the aim of improving information discovery and information sharing using the Semantic Web technologies. We also provide different Adaptive Hypermedia (AH) methods using ontology-based user models. In our second contribution, we introduce and implement a novel personalized Semantic Web browser, called SemWeB which is a browser that augments Web documents with metadata. It creates and personalizes context-based hyperlinks and data using ontologies. We have also developed a new behaviour-based user model for Web-based personalization which supports different AH methods. In addition, a novel semantic relatedness measure is proposed. The evaluations showed that our contributions to the development of hypertext systems using Semantic Web technologies are successfully applied for context-based link creation and personalization.

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18

Paiu, Raluca. "Exploiting tag information for search and personalization". Hannover Technische Informationsbibliothek und Universitätsbibliothek Hannover, 2010. http://d-nb.info/1001050592/34.

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19

Friberg, Christoffer. "Cloud-based UI personalization : Accessibility made easier". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167492.

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This project had two primary goals. The first was to use existing knowledge about mobile accessibility, smartphone accessibility and touchscreen usability to improve and extend the usability of two Total Conversation applications for Android. Total Conversation is the combination of video, audio and real-time text conversations frequently used by the deaf and hard of hearing. Total Conversation has wide-spread support in both the mobile and PC realms, but the primary benefit is that it’s also supported by standalone video phones. The second goal was to integrate these applications with the EU-funded Cloud4all framework. The purpose of Cloud4all is to develop a framework for automatic cloud-based user interface customization based on the individual needs and preferences of each user. Both goals were met and functional prototypes of both applications were developed and tested. A Cloud4all user can log in and have their cloud-stored preferences applied with no further action taken.Supported Cloud4all preferences are font size and high contrast theme selection. Support for icon size adjustments is implemented but not yet supported by the Cloud4all framework.
Detta examensarbete hade två huvudsakliga mål. Det första var att använda tidigare arbeten inom mobil tillgänglighet, smartphone-tillgänglighet ochtouch-skärmtillgänglighet som grund för att förbättra användbarheten och tillgängligheten i två Total Konversationsappar. Total Konversation är kombinationen av video-, ljud- och realtidstextsamtal, som till stor del används av döva och hörselskadade. Total Konversation har brett stöd både i datorer och mobila enheter, samt i dedikerade videotelefoner. Det andra målet är att integrera dessa två appar med det EU-sponsrade projektet Cloud4all och den plattform som utvecklats där. Syftet med Cloud4all är att utveckla en plattform för automatisk anpassning av användargränssnitt baserat på varje enskild användares specifika behov. Båda dessa mål har uppnåtts, och väl fungerande prototyper av integrerade applikationer har utvecklats. En Cloud4all-användare kan logga in och automatiskt få användargränssnittet anpassat efter sina moln-lagrade inställningar. De Cloud4all-stödda inställningar som implementerats är textstorlek samt val av högkontrasttema. Utöver det finns fullt stöd för att ändra ikon- och knappstorlekar, men det stöds ännu inte av Cloud4all- plattformen.

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20

Shankar, Anil K. "Simple user-context for better application personalization". abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433351.

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21

Teevan, Jaime B. (Jaime Brooks) 1976. "Supporting finding and re-finding through personalization". Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/38536.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 165-176).
Although one of the most common uses for the Internet to search for information, Web search tools often fail to connect people with what they are looking for. This is because search tools are designed to satisfy people in general, not the searcher in particular. Different individuals with different information needs often type the same search terms into a search box and expect different results. For example, the query "breast cancer" may be used by a student to find information on the disease for a fifth grade science report, and by a cancer patient to find treatment options. This thesis explores how Web search personalization can help individuals take advantage of their unique past information interactions when searching. Several studies of search behavior are presented and used to inform the design of a personalized search system that significantly improves result quality. Without requiring any extra effort from the user, the system is able to return simple breast cancer tutorials for the fifth grader's "breast cancer" query, and lists of treatment options for the patient's. While personalization can help identify relevant new information, new information can create problems re-finding when presented in a way that does not account for previous information interactions.
(cont.) Consider the cancer patient who repeats a search for breast cancer treatments: she may want to learn about new treatments while reviewing the information she found earlier about her current treatment. To not interfere with refinding, repeat search results should be personalized not by ranking the most relevant results first, but rather by ranking them where the user most expects them to be. This thesis presents a model of what people remember about search results, and shows that it is possible to invisibly merge new information into previously viewed search result lists where information has been forgotten. Personalizing repeat search results in this way enables people to effectively find both new and old information using the same search result list.
by Jaime Teevan.
Ph.D.

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22

Vieira, André Fonseca dos Santos Dias. "Context-aware personalization environment for mobile computing". Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8649.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática
Currently, we live in a world where the amount of on-line information vastly outstrips any individual’s capability to survey it. Filtering that information in order to obtain only useful and interesting information is a solution to this problem. The mobile computing area proposes to integrate computation in users’ daily activities in an unobtrusive way, in order to guarantee an improvement in their experience and quality of life. Furthermore, it is crucial to develop smaller and more intelligent devices to achieve this area’s goals, such as mobility and energy savings. This computing area reinforces the necessity to filter information towards personalization due to its humancentred paradigm. In order to attend to this personalization necessity, it is desired to have a solution that is able to learn the users preferences and needs, resulting in the generation of profiles that represent each style of interaction between a user and an application’s resources(e.g. buttons and menus). Those profiles can be obtained by using machine learning algorithms that use data derived from the user interaction with the application, combined with context data and explicit user preferences. This work proposes an environment with a generic context-aware personalization model and a machine learning module. It is provided the possibility to personalize an application, based on user profiles obtained from data, collected from implicit and explicit user interaction. Using a provided personalization API (Application Programming Interface) and other configuration modules, the environment was tested on LEY (Less energy Empowers You), a persuasive mobile-based serious game to help people understand domestic energy usage.

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23

Santos, Pedro Emanuel Albuquerque e. Baptista dos. "Personalization platform for multimodal ubiquitous computing applications". Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/11063.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática
We currently live surrounded by a myriad of computing devices running multiple applications. In general, the user experience on each of those scenarios is not adapted to each user’s specific needs, without personalization and integration across scenarios. Moreover, developers usually do not have the right tools to handle that in a standard and generic way. As such, a personalization platform may provide those tools. This kind of platform should be readily available to be used by any developer. Therefore, it must be developed to be available over the Internet. With the advances in IT infrastructure, it is now possible to develop reliable and scalable services running on abstract and virtualized platforms. Those are some of the advantages of cloud computing, which offers a model of utility computing where customers are able to dynamically allocate the resources they need and are charged accordingly. This work focuses on the creation of a cloud-based personalization platform built on a previously developed generic user modeling framework. It provides user profiling and context-awareness tools to third-party developers. A public display-based application was also developed. It provides useful information to students, teachers and others in a university campus as they are detected by Bluetooth scanning. It uses the personalization platform as the basis to select the most relevant information in each situation, while a mobile application was developed to be used as an input mechanism. A user study was conducted to assess the usefulness of the application and to validate some design choices. The results were mostly positive.

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24

Rashad, Hisham S. M. "Trust Management Systems: Reference Architecture and Personalization". Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/88457.

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Trust is the cornerstone of success in any relationship between two or more parties. Generally, we do not socialize, seek advice, consult, cooperate, buy or sell goods and services from/to others unless we establish some level of mutual trust between interacting parties. When e-commerce was merging infancy, the concept of trusting an entity in a virtual world was a huge obstacle. Gradually, increasingly-sophisticated, largely generic reputation scoring and management systems were embedded into the evolving marketplaces. Current technologies to include cloud computing, social networking, and mobile applications, coupled with the explosion in storage capacity and processing power, are evolving large-scale global marketplaces for a wide variety of resources and services, such as Amazon.com, BitTorrent, WebEx and Skype. In such marketplaces, user entities, or users for short; namely, consumers, providers and brokers, are largely autonomous with vastly diverse requirements, capabilities and trust profiles. Users' requirements may include service quality levels, cost, ease of use, etc. Users' capabilities may include assets owned or outsourced. Trustors' profiles may include what is promised and commitments to keep these promises. In such a large-scale heterogeneous marketplace, the trustworthy interactions and transactions in services and resources constitute a challenging endeavor. Currently, solving such issues generally adopts a "one-size fits all" trust models and systems. Such approach is limiting due to variations in technology, conflicts between users' requirements and/or conflicts between user requirements and service outcomes. Additionally, this approach may result in service providers being overwhelmed by adding new resources to satisfy all possible requirements, while having no information or guarantees about the level of trust they gain in the network. Accordingly, we hypothesize the need for personalizable customizable Trust Management Systems (TMSs) for the robustness and wide-scale adoption of large-scale marketplaces for resources and services. Most contemporary TMSs suffer from the following drawbacks: • Oblivious to diversities in trustors' requirements, • Primarily utilize feedback and direct or indirect experience as the only form of credentials and trust computations, • Trust computation methodologies are generally hardcoded and not reconfigurable, • Trust management operations, which we identify as monitoring, data management, analysis, expectation management, and decision making, are tightly coupled. Such coupling impedes customizability and personalization, and • Do not consider context in trust computations, where trust perspectives may vary from a context to another. Given these drawbacks and the large scale of the global marketplace of resources and services, a reference architecture for trust management systems is needed, which can incorporate current systems and may be used in guidance and development of a wide spectrum of trust management systems ranging from un-personalized to fully personalized systems. Up to our knowledge, no TMS reference architecture exists in the literature. In this dissertation, we propose a new Reference Architecture for Trust Management (RATM). The proposed reference architecture applies separation of concern among trust management operations; namely, decision expectation, analytics, data management and monitoring. RATM defines trust management operations through five reconfigurable components which collectively can be used to implement a wide spectrum of trust management systems ranging from generic to highly personalized systems. We used RATM for trust personalization, where we propose a Personalized Trust Management System (PTMS) based on RATM. We evaluated PTMS's scalability and demonstrated its effectiveness, efficiency and resilience by contrasting against a Generic Trust Management System (GTMS). We used two case studies for our evaluations; namely, BitTorrent and a video conferencing application. Intellectual Merit In this work, we propose RATM, a reference architecture for trust management systems that can be used to implement a wide variety of trust management systems ranging from generic systems (un-personalized) to highly personalized systems. We assume service-based environment where consumers, brokers and service providers are interacting and transacting in services and resources to satisfying their own trust requirements. We used RATM to implement a personalized trust management system (TMS). The main contributions of this work are as follows: • Proposing RATM for the guidance and development of a wide spectrum of TMSs ranging from un-personalized to fully personalized systems, and • Utilizing our RATM to propose and implement a personalized, scalable TMS with varying trust computation models. Broader Impact RATM provides reference architecture for trust management which can be used to develop and evaluate a wide spectrum of TMSs. Personalization, in general, paves the road for reaching high levels of satisfaction, where interacting parties' requirements are individually considered and thus consumers are served the best suited service(s). Hence, we claim that PTMS would largely enhance large-scale heterogeneous systems offering services and resources. This could lead to more cooperation, more transactions, more satisfaction, less malicious behavior and lower costs.
PHD

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25

Pfiffelmann, Jean. "Identification-based personalization effects in recruitment advertising". Thesis, Lyon, 2020. http://www.theses.fr/2020LYSE3026.

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Attirer les talents constitue aujourd’hui un défi pour les organisations. De nombreux recruteurs se tournent vers les médias sociaux pour communiquer sur leurs postes à pourvoir en ciblant les employés potentiels. Cependant, cibler les bons candidats n’est pas nécessairement suffisant pour attirer leur attention dans un environnement encombré, augmenter les taux de clics sur les publicités, ou favoriser les candidatures. Désormais, les médias sociaux offrent des options de personnalisation aux publicitaires, ce qui pourrait améliorer l’efficacité publicitaire. Par exemple, les recruteurs peuvent intégrer le prénom et la photographie des candidats dans la publicité ; une tactique de personnalisation appelée « Identification ». Ce travail doctoral, positionné en marketing des ressources humaines, a pour objectif de répondre à la question suivante : comment l’identification d’une publicité de recrutement (en tant que tactique de personnalisation) influence-t-elle les employés potentiels ? D'un point de vue théorique, ce travail doctoral contribue à la compréhension de la publicité de recrutement personnalisée. Nous avons mis en lumière les mécanismes de fonctionnement de la personnalisation des publicités de recrutement en révélant plusieurs processus de médiation et de modération. En nous appuyant sur des mesures eye-tracking, nos résultats montrent que la personnalisation augmente l’attention visuelle portée à la publicité. L’attention visuelle dédiée à la publicité diminue l’intention des employés potentiels de candidater à l’offre d’emploi, car elle génère une perception d’intrusion. Cependant, la personnalisation conduit à plusieurs résultats positifs, tels que la crédibilité de la publicité, la perception d'un traitement de considération, ou le divertissement perçu. Bien que ces perceptions positives permettent de favoriser l’intention de candidater, elles varient en fonction des traits de personnalité des destinataires et du contexte d'exposition à la publicité. Cette thèse peut non seulement bénéficier aux recruteurs désireux de recevoir plus de candidatures tout en optimisant leurs dépenses publicitaires, mais également aux managers de médias sociaux qui se doivent de concevoir des outils publicitaires efficaces pour augmenter leurs revenus principalement dépendants des dépenses publicitaires des annonceurs
Attracting talent is a perennial difficulty for organizations. Many recruiters turn to social media to advertise job openings by targeting potential employees. However, targeting the right candidates may not be enough to grab their attention in a cluttered environment, augment clicks-through rates on the ads, or increase job pursuit. Henceforth, social media offers advertisers with personalization tools that could improve advertising effectiveness. For instance, recruiters can embed the candidates’ first name and photograph in the ad, a personalization tactic known as “identification.” This doctoral work, positioned in human resources marketing, aims to answer the following question: How does identification-based personalized recruitment advertising influence potential employees’ responses? From a theoretical standpoint, this doctoral work contributes to the understanding of the working mechanisms of recruitment ad personalization by revealing several mediating processes and boundary conditions. We show that personalization increases visual attention to the ad using eye-tracking measures. The enhanced visual attention to the ad leads to a perception of intrusiveness, decreasing job-pursuit intention. However, personalization leads to several positive outcomes, such as ad credibility, perceptions of considerate treatment, or perceived entertainment. While these positive perceptions benefit job-pursuit intention, they vary according to receivers’ personality traits and the exposure context to the ad. This dissertation may help recruiters aiming to improve their application pool while optimizing their recruitment spending and social media managers who need to design effective advertising tools to increase their revenue based on recruiters’ ad spending

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26

Döring, Sven. "Personalization of the search process in tourism". kostenfrei, 2008. http://d-nb.info/989393860/34.

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Anderson, Corin R. "A machine learning approach to Web personalization /". Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6875.

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Haraty, Mona. "Designing for authoring and sharing of advanced personalization". Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59508.

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Interactive technologies have become prevalent in all aspects of life including managing our tasks, looking for information, and connecting with others. We often adapt our behaviors, consciously or unconsciously, to accommodate the technology. The unique nature of our needs and preferences, and how they change over time are better supported with technologies that are designed to be personalizable. Lack of personalization facilities limits our range of behaviors. In this dissertation, we focus on understanding and supporting differences in individuals’ behaviors through forms of personalization that are beyond choosing among a set of predefined options, by allowing users to author new functionalities. We refer to such personalizations as “advanced personalizations.” Authoring advanced personalizations, when supported, is often time-consuming and requires programming skills. Consequently, either because of lack of ability or time, many users take advantage of personalizations created and shared by others. The overarching goal of this dissertation is to design for authoring and sharing of advanced personalizations. We explore this goal in the domain of personal task management (PTM), where rich individual differences deeply influence user behavior and tool use. First, to gain insights into individual differences in PTM as well as changes in PTM behaviors over time, we conducted a series of studies: a focus group and contextual interviews in an academic setting, a large survey questionnaire with a broader population, and follow-up interviews with some of the survey respondents. These studies provide insights into different types of advanced perosnalizations that a PTM tool needs to support. Next, we designed a personalizable PTM tool with two key components for authoring advanced personalizations, building on ideas from end user programming approaches, and following theoretical guidelines on designing personalizable tools such as meta-design guidelines. A controlled user study of our design revealed opportunities and challenges in supporting advanced personalization, and our detailed design process provides a practical starting point for designing personalizable tools. Finally, through studying personalization sharing practices, we characterized the multi-faceted nature of online personalization sharing ecosystems, which include multiple components for hosting personalizations, discussing, and managing them. Our findings also highlight tradeoffs and design considerations in such ecosystems.
Science, Faculty of
Computer Science, Department of
Graduate

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29

Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning". Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.

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Les ordinateurs peuvent-ils enseigner ? Pour répondre à cette question, la recherche dans les Systèmes Tuteurs Intelligents est en pleine expansion parmi la communauté travaillant sur les Technologies de l'Information et de la Communication pour l'Enseignement (TICE). C'est un domaine qui rassemble différentes problématiques et réunit des chercheurs venant de domaines variés, tels que la psychologie, la didactique, les neurosciences et, plus particulièrement, le machine learning. Les technologies numériques deviennent de plus en plus présentes dans la vie quotidienne avec le développement des tablettes et des smartphones. Il semble naturel d'utiliser ces technologies dans un but éducatif. Cela amène de nombreuses problématiques, telles que comment faire des interfaces accessibles à tous, comment rendre des contenus pédagogiques motivants ou encore comment personnaliser les activités afin d'adapter le contenu à chacun. Au cours de cette thèse, nous avons développé des méthodes, regroupées dans un framework nommé HMABITS, afin d'adapter des séquences d'activités pédagogiques en fonction des performances et des préférences des apprenants, dans le but de maximiser leur vitesse d'apprentissage et leur motivation. Ces méthodes utilisent des modèles computationnels de motivation intrinsèque pour identifier les activités offrant les plus grands progrès d'apprentissage, et utilisent des algorithmes de Bandits Multi-Bras pour gérer le compromis exploration/exploitation à l'intérieur de l'espace d'activité. Les activités présentant un intérêt optimal sont ainsi privilégiées afin de maintenir l'apprenant dans un état de Flow ou dans sa Zone de Développement Proximal. De plus, certaines de nos méthodes permettent à l'apprenant de faire des choix sur des caractéristiques contextuelles ou le contenu pédagogique de l'application, ce qui est un vecteur d'autodétermination et de motivation. Afin d'évaluer l'efficacité et la pertinence de nos algorithmes, nous avons mené plusieurs types d'expérimentation. Nos méthodes ont d'abord été testées en simulation afin d'évaluer leur fonctionnement avant de les utiliser dans d'actuelles applications d'apprentissage. Pour ce faire, nous avons développé différents modèles d'apprenants, afin de pouvoir éprouver nos méthodes selon différentes approches, un modèle d'apprenant virtuel ne reflétant jamais le comportement d'un apprenant réel. Les résultats des simulations montrent que le framework HMABITS permet d'obtenir des résultats d'apprentissage comparables et, dans certains cas, meilleurs qu'une solution optimale ou qu'une séquence experte. Nous avons ensuite développé notre propre scénario pédagogique et notre propre serious game afin de tester nos algorithmes en situation réelle avec de vrais élèves. Nous avons donc développé un jeu sur la thématique de la décomposition des nombres, au travers de la manipulation de la monnaie, pour les enfants de 6 à 8 ans. Nous avons ensuite travaillé avec le rectorat et différentes écoles de l'académie de bordeaux. Sur l'ensemble des expérimentations, environ 1000 élèves ont travaillé sur l'application sur tablette. Les résultats des études en situation réelle montrent que le framework HMABITS permet aux élèves d'accéder à des activités plus diverses et plus difficiles, d'avoir un meilleure apprentissage et d'être plus motivés qu'avec une séquence experte. Les résultats montrent même que ces effets sont encore plus marqués lorsque les élèves ont la possibilité de faire des choix
Can computers teach people? To answer this question, Intelligent Tutoring Systems are a rapidly expanding field of research among the Information and Communication Technologies for the Education community. This subject brings together different issues and researchers from various fields, such as psychology, didactics, neurosciences and, particularly, machine learning. Digital technologies are becoming more and more a part of everyday life with the development of tablets and smartphones. It seems natural to consider using these technologies for educational purposes. This raises several questions, such as how to make user interfaces accessible to everyone, how to make educational content motivating and how to customize it to individual learners. In this PhD, we developed methods, grouped in the aptly-named HMABITS framework, to adapt pedagogical activity sequences based on learners' performances and preferences to maximize their learning speed and motivation. These methods use computational models of intrinsic motivation and curiosity-driven learning to identify the activities providing the highest learning progress and use Multi-Armed Bandit algorithms to manage the exploration/exploitation trade-off inside the activity space. Activities of optimal interest are thus privileged with the target to keep the learner in a state of Flow or in his or her Zone of Proximal Development. Moreover, some of our methods allow the student to make choices about contextual features or pedagogical content, which is a vector of self-determination and motivation. To evaluate the effectiveness and relevance of our algorithms, we carried out several types of experiments. We first evaluated these methods with numerical simulations before applying them to real teaching conditions. To do this, we developed multiple models of learners, since a single model never exactly replicates the behavior of a real learner. The simulation results show the HMABITS framework achieves comparable, and in some cases better, learning results than an optimal solution or an expert sequence. We then developed our own pedagogical scenario and serious game to test our algorithms in classrooms with real students. We developed a game on the theme of number decomposition, through the manipulation of money, for children aged 6 to 8. We then worked with the educational institutions and several schools in the Bordeaux school district. Overall, about 1000 students participated in trial lessons using the tablet application. The results of the real-world studies show that the HMABITS framework allows the students to do more diverse and difficult activities, to achieve better learning and to be more motivated than with an Expert Sequence. The results show that this effect is even greater when the students have the possibility to make choices

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Cloarec, Julien. "The Personalization-Privacy Paradox in the Attention Economy". Thesis, Toulouse 1, 2019. http://www.theses.fr/2019TOU10049.

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Le résumé en français n'a pas été communiqué par l'auteur
The personalization-privacy paradox operates as a continuous, tension-charged cycle. Although consumers expect and consider the value of personalization, marketers’ exploitation of consumers’ personal information to provide personalization raises privacy concerns. Consumers, then, form a reluctance to provide personal information for personalization. Some researchers have attempted to enlist IT solutions to address this issue (e.g., anonymizing techniques and peer-to-peer communication), but these solutions proved ineffective as they were too sophisticated for the average consumer. Consequently, the personalization-privacy paradox, which emerged with the advent of mobile technologies, must be more theoretically founded. To date, the information systems literature primarily explicates the issue by applying myriad micro-oriented theories (e.g., privacy calculus theory, game theory, and information boundary theory). The first chapter suggests that the personalization-privacy paradox should also be examined at a macro level—through the lens of the “attention economy.” Investigating the relationship among personalization, privacy, and attention, brings insights regarding the ecology of attention, choice architecture, and stylistic devices and suggests implications for research and practice. The second chapter builds on both social exchange and construal level theories to investigate the extent to which happiness drives the personalization–privacy trade-off decision, as well as the moderating role of experience sharing frequency as a proxy for reciprocity. An online survey administered to a representative sample of French consumers (n = 649) largely confirms the predictions: happiness is the strongest driver of willingness to disclose information in exchange for personalization, surpassing conventional privacy-related constructs (e.g., trust and risk beliefs). Based on social exchange theory and the engagement literature, the third chapter investigates the influence of SNS activity (i.e., collaborative engagement) on users’ willingness to disclose information for personalization (e.g., a form of engagement with SNS platforms). The model is tested using the same dataset as before (n = 649). The results show that happiness with the Internet increases SNS use frequency through SNS literacy, and trust beliefs (information collection concerns) positively (negatively) impact the strength of the indirect relationship between SNS use frequency and willingness to disclose information for personalization via SNS posting frequency. The fourth and last chapter examines the importance of empowering consumers regarding their privacy. While complex, it is necessary to keep on investigating the ambivalent effect of privacy controls because the trade-off between advertising effectiveness and consumer privacy is at the core of the platform economy, which revenues rely on advertising. The author conducted an online survey among French-speaking Facebook users (n = 227). Through a privacy calculus lens, the author adopted a within-subject design to test the effect of education on privacy controls on satisfaction with Facebook ads. The results show that education on privacy controls indirectly affect the satisfaction with Facebook ads via privacy concerns (negative), fairness (positive), and attention quality (positive)

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Md, Amin Mohd Afandi. "A user acceptance model of web personalization systems". Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/98965/1/Mohd%20Afandi%20bin%20Md%20Amin%20Thesis.PDF.

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Research on web personalization techniques for collecting and analysing web data in order to deliver personalized information to users is in an advanced state. Many metrics from the computational intelligence field have been developed to evaluate the algorithmic performance of Web Personalization Systems (WPSs). However, measuring the success of a WPS in terms of user acceptance is difficult until the WPS is deployed in practice. In summary, many techniques exist for delivering personalized information to a user, but a comprehensive measure of the success in WPSs in terms of human interaction and behaviour does not exist. This study aims to develop a framework for measuring user acceptance of WPSs from a user perspective. The proposed framework is based on the unified theory of acceptance and use of technology (UTAUT). The antecedents of user acceptance are described by indicators based on four key constructs, i.e. performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). All these constructs are underpinned by Information Systems (IS) theories that determine the intention to use (BI) and the actual use (USE) of a technology. A user acceptance model was proposed and validated using structural equation modelling (SEM) via the partial least squares path modelling (PLS-PM). Four user characteristics (i.e. gender, age, skill and experience) have been chosen for testing the moderating effects of the four constructs. The relationship between the four constructs in regard to BI and USE has been validated through moderating effects, in order to present an overall view of the extent of user acceptance of a WPS. Results from response data analysis show that the acceptance of a WPS is determined through PE, EE SI, and FC. The gender of a user was found to moderate the relationship between performance expectancy of a WPS and their behavioural intention in using a WPS. The effect of behavioural intention on the use of WPS is higher for a group of females than for males. Furthermore, the proposed model has been tested and validated for its explanation power of the model and effect size. The current study concluded that predictive relevance of intention to use a WPS is more effective than the actual WPS usage, which indicated that intention to use has more prediction power for describing a user acceptance of a WPS. The implications of these measures from the computational intelligent point of view are useful when a WPS is implemented. For example, the designer of a WPS should consider personalized design features that enable the delivery of relevant information, sharing to other users, and accessibility across many platforms, Such features create a better web experience and a complete security policy. These measures can be utilized to obtain a higher attention rate and continued use by a user; the features that define user acceptance of a WPS.

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32

Diliwi, Avesta, Christopher Ullberg y Johanna Jevinger. "Likelihood of Using Online Personalization Services : An Explanatory Study". Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-65319.

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Bachelor Thesis in Business Administration. Bachelor of Science with Specialization in Marketing – Main Field of Study: Business Administration. School of Business and Economics at Linnaeus University, Course Code 2FE21E, 2017. Title: Likelihood of Using Online Personalization Services: An Explanatory Study Authors: Avesta Diliwi, Christopher Ullberg and Johanna Jevinger Supervisor: Michaela Sandell Examiner: Åsa Devine Background: Online personalization is the result of the rapid technological and digital development where consumers are provided products, services and content based on their individual preferences. Various research has been conducted regarding what factors influence the utilization and acceptance of personalization but does not provide a holistic view on the unified relationship of the recurrent variables of value for personalization, concern for privacy and trust building factors towards likelihood of using online personalization services. Purpose: The purpose of this research is to explain the relationship of value for personalization, concern for privacy, and trust building factors with the likelihood of using online personalization services. Methodology: This research replicated Chellappa and Sin’s (2005) research by modifying their theoretical model and testing it in another context. An explanatory, deductive, quantitative research approach and cross-sectional research design were utilized within this research, where self-completed questionnaires were distributed online with a number of 228 valid responses collected. Findings: The findings demonstrate that the new theoretical model is significant and that it explains the likelihood of using online personalization services with 62,3%. Value for personalization and concern for privacy are considered highly significant and are thus accepted hypotheses, while trust building factors is not considered significant and therefore rejected. Conclusion: This research provides an insight into consumers’ usage decision in regards to likelihood of using personalization. It also provides a furthering on prior research in regards to a theoretical development, the modified model tested in a new context, but also in the findings in how the three independent variables affect the dependent variable. In addition, this research provides support for practitioners of online personalization services to understand which factors actually affect consumers’ usage decision, and can potentially develop strategies accordingly. Keywords: Personalization; Online Personalization Services; Likelihood of Using Online Personalization Services; Value for Personalization; Concern for Privacy; Trust Building Factors

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Paiu, Raluca [Verfasser]. "Exploiting tag information for search and personalization / Raluca Paiu". Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover, 2010. http://d-nb.info/1001050592/34.

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34

Rudinski, Sanja. "Consumer Perception of Brand Personalization : Adbusters as Anti-Brand". Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-10149.

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Thesis purpose: The thesis purpose is to examine the interrelationship of consumer and brand relation. The main objective is to research how consumers’ perception of brand personalization before and after seeing Adbusters subvertisement ads influences their relation to brands. Methodology: The thesis has deductive quantitative research approach, since it starts from theory to gain more understanding of the research area. The data is collected through an online questionnaire. Theoretical perspective: This study is based upon the general brand and branding definitions, with focus on brand personality, consumer perception and findings within the study of Adbusters. Empirical data: The study is investigated quantitatively. An online questionnaire was used to gather the empirical data. Conclusion: This study provides a broad overview about consumer perception of brand personalized commercial ads and what is their relation to brands after seeing Adbusters subvertisement ads. The research contributes to brand personalization theory and Adbusters studies and demonstrates that Adbusters influence on brand personalization depends on how strong the consumer-brand relationship is. However, it is also shown that consumers are inconsistent in their perception of brand personas and that relation to brands is highly brand-specific.

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Vu, Manh Vu Duy. "Online Education : A study about students motivations and personalization". Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43479.

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Because of the pandemic Coronavirus and proposals from the Swedish Public Health Agency, schools in Sweden had to adjust their teaching to distance education. The purpose of this study is to study students' motivation, understand what is inspiring for students, many students lack inspiration. Personalization in education, what kind of support could we give the students. Understand personalization from the students perspective different students need different help, make the students feel like they get personalized help. Hands on, how do we apply things that are theoretical to the practical. Through semi-structured interviews, this study has analyzed how students have experienced distance education, difficulties, barriers, opportunities among others have been looked at. The teaching followed essentially the same pattern. The teachers and students followed the same schedule as the pre-transfer, and had contact through a video call. From that point forward, the teacher had some type of show of new material, through a live video call. At that point the student generally followed up with working on their own, where they had the chance to contact the teacher to pose questions. The results of this study showed that online education had a negative effect on the quality of teaching. The students felt a lack of motivation and a greater personal responsibility. There were also some positive aspects of online education as it could be more adaptable and suit more students. Travel time could be reduced and the time could be spent on something else. The idea of having an online education platform was a positive idea for most of the students. As working with a real company and gaining real life experience where school sometimes can lack was seen as a positive aspect.

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36

Flory, Long Mrs. "A WEB PERSONALIZATION ARTIFACT FOR UTILITY-SENSITIVE REVIEW ANALYSIS". VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3739.

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Online customer reviews are web content voluntarily posted by the users of a product (e.g. camera) or service (e.g. hotel) to express their opinions about the product or service. Online reviews are important resources for businesses and consumers. This dissertation focuses on the important consumer concern of review utility, i.e., the helpfulness or usefulness of online reviews to inform consumer purchase decisions. Review utility concerns consumers since not all online reviews are useful or helpful. And, the quantity of the online reviews of a product/service tends to be very large. Manual assessment of review utility is not only time consuming but also information overloading. To address this issue, review helpfulness research (RHR) has become a very active research stream dedicated to study utility-sensitive review analysis (USRA) techniques for automating review utility assessment. Unfortunately, prior RHR solution is inadequate. RHR researchers call for more suitable USRA approaches. Our current research responds to this urgent call by addressing the research problem: What is an adequate USRA approach? We address this problem by offering novel Design Science (DS) artifacts for personalized USRA (PUSRA). Our proposed solution extends not only RHR research but also web personalization research (WPR), which studies web-based solutions for personalized web provision. We have evaluated the proposed solution by applying three evaluation methods: analytical, descriptive, and experimental. The evaluations corroborate the practical efficacy of our proposed solution. This research contributes what we believe (1) the first DS artifacts to the knowledge body of RHR and WPR, and (2) the first PUSRA contribution to USRA practice. Moreover, we consider our evaluations of the proposed solution the first comprehensive assessment of USRA solutions. In addition, this research contributes to the advancement of decision support research and practice. The proposed solution is a web-based decision support artifact with the capability to substantially improve accurate personalized webpage provision. Also, website designers can apply our research solution to transform their works fundamentally. Such transformation can add substantial value to businesses.

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37

Sadasivam, Rajani Shankar. "An architecture framework for composite services with process-personalization". Birmingham, Ala. : University of Alabama at Birmingham, 2007. https://www.mhsl.uab.edu/dt/2009r/sadasivam.pdf.

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Thesis (Ph. D.)--University of Alabama at Birmingham, 2007.
Title from PDF title page (viewed Feb. 4, 2010). Additional advisors: Barrett R. Bryant, Chittoor V. Ramamoorthy, Jeffrey H. Kulick, Gary J. Grimes, Gregg L. Vaughn, Murat N. Tanju. Includes bibliographical references (p. 161-183).

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38

Garrigós, Irene. "A-OOH: extending web application design with dynamic personalization". Doctoral thesis, Universidad de Alicante, 2008. http://hdl.handle.net/10045/9645.

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This work has been partially supported by the ESPIA (TIN2007-67078) project from the Spanish Ministry of Education and Science, by the DADASMECA project (GV05/220) from the Valencia Ministry of Enterprise, University and Science (Spain), and by the DADS (PBC-05-012-2) project from the Castilla La Mancha Ministry of Education and Science (Spain).

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39

FERRARI, ANNA. "Personalization of Human Activity Recognition Methods using Inertial Data". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/305222.

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Recognizing human activities and monitoring population behavior are fun- damental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, lifestyle and behavior tracking are some of the main applications which require the recognition of activities. Activity recognition involves many phases, i.e. the collection, the elaboration and the analysis of information about human activities and behavior. These tasks can be fulfilled manually or automatically, even though a human-based recognition system is not long-term sustainable and scalable. Nevertheless, transforming a human-based recognition system to computer- based automatic system is not a simple task because it requires dedicated hardware and a sophisticated engineering computational and statistical techniques for data preprocessing and analysis. Recently, considerable changes in tech- nologies are largely facilitating this transformation. Indeed, new hardwares and softwares have drastically modified the activity recognition systems. For example, Micro-Electro-Mechanical Systems (MEMS) progress has enabled a reduction in the size of the hardware. Consequently, costs have decreased. Size and cost reduction allows to embed sophisticated sensors into simple devices, such as phones, watches, and even into shoes and clothes, also called wearable devices. Furthermore, low costs, lightness, and small size have made wearable devices’ highly pervasive and accelerated their spread among the population. Today, a very small part of the world population doesn’t own a smartphone. According to Digital 2020: Global Digital Overview, more than 5.19 billion people now use mobile phones. Among the western countries, smartphones and smartwatches are gadgets of people everyday life. The pervasiveness is an undoubted advantage in terms of data generation. Huge amount of data, that is big data, are produced every day. Furthermore, wearable devices together with new advanced software technologies enable data to be sent to servers and instantly analyzed by high performing computers. The availability of big data and new technology improvements, permitted Artificial Intelligence models to rise. In particular, machine learning and deep learning algorithms are predominant in activity recognition. Together with technological and algorithm innovations, the Human Ac- tivity recognition (HAR) research field has born. HAR is a field of research which aims at automatically recognizing people’s physical activities. HAR investigates on the selection of the best hardware, e. g. the best devices to be used for a given application, on the choice of the software to be dedicated to a specific task, and on the increasing of the algorithm performances. HAR has been a very active field of research for years and it is still considered one of the most promising research topic for a large spectrum of ap- plications. In particular, it remains a very challenging research field for many reasons. The selection of devices and sensors, the algorithm’s performances, the collection and the preprocessing of the data, all are requiring further investigation to improve the overall activity recognition system performances. In this work, two main aspects have been investigated: • the benefits of personalization on the algorithm performances, when trained on small size datasets: one of the main issue concerning HAR research community is the lack of the availability of public dataset and labelled data. [...] • a comparison of the performances in HAR obtained both from tradi- tional and personalized machine learning and deep learning techniques.[...]
Recognizing human activities and monitoring population behavior are fun- damental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, lifestyle and behavior tracking are some of the main applications which require the recognition of activities. Activity recognition involves many phases, i.e. the collection, the elaboration and the analysis of information about human activities and behavior. These tasks can be fulfilled manually or automatically, even though a human-based recognition system is not long-term sustainable and scalable. Nevertheless, transforming a human-based recognition system to computer- based automatic system is not a simple task because it requires dedicated hardware and a sophisticated engineering computational and statistical techniques for data preprocessing and analysis. Recently, considerable changes in tech- nologies are largely facilitating this transformation. Indeed, new hardwares and softwares have drastically modified the activity recognition systems. For example, Micro-Electro-Mechanical Systems (MEMS) progress has enabled a reduction in the size of the hardware. Consequently, costs have decreased. Size and cost reduction allows to embed sophisticated sensors into simple devices, such as phones, watches, and even into shoes and clothes, also called wearable devices. Furthermore, low costs, lightness, and small size have made wearable devices’ highly pervasive and accelerated their spread among the population. Today, a very small part of the world population doesn’t own a smartphone. According to Digital 2020: Global Digital Overview, more than 5.19 billion people now use mobile phones. Among the western countries, smartphones and smartwatches are gadgets of people everyday life. The pervasiveness is an undoubted advantage in terms of data generation. Huge amount of data, that is big data, are produced every day. Furthermore, wearable devices together with new advanced software technologies enable data to be sent to servers and instantly analyzed by high performing computers. The availability of big data and new technology improvements, permitted Artificial Intelligence models to rise. In particular, machine learning and deep learning algorithms are predominant in activity recognition. Together with technological and algorithm innovations, the Human Ac- tivity recognition (HAR) research field has born. HAR is a field of research which aims at automatically recognizing people’s physical activities. HAR investigates on the selection of the best hardware, e. g. the best devices to be used for a given application, on the choice of the software to be dedicated to a specific task, and on the increasing of the algorithm performances. HAR has been a very active field of research for years and it is still considered one of the most promising research topic for a large spectrum of ap- plications. In particular, it remains a very challenging research field for many reasons. The selection of devices and sensors, the algorithm’s performances, the collection and the preprocessing of the data, all are requiring further investigation to improve the overall activity recognition system performances. In this work, two main aspects have been investigated: • the benefits of personalization on the algorithm performances, when trained on small size datasets: one of the main issue concerning HAR research community is the lack of the availability of public dataset and labelled data. [...] • a comparison of the performances in HAR obtained both from tradi- tional and personalized machine learning and deep learning techniques.[...]

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40

Lu, Minghao. "Web personalization based on association roles finding on both static and dynamic Web data". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/4162.

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The explosive and continuous growth in the size and use of the World Wide Web is at the basis of the great interest into web usage mining techniques in both research and commercial areas. In particular, the need for predicting the user’s needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalization. In this thesis, we introduce a new framework that takes advantage of the sophisticated association rule finding web mining technology on both dynamic user activities over a web site, such as navigational behavior, and static information, such as user profiles and web content. We also provide a novel personalization selection system which allows users to choose the most suitable profile for them in any given period of time. In order to examine the viability of our framework, we incorporate and implement it over a well designed simulation environment. Moreover, our experiment proves that our framework provides an overall better web personalization service in terms of both recommendation accuracy and user satisfaction.

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41

Abel, Fabian [Verfasser]. "Contextualization, user modeling and personalization in the social web : from social tagging via context to cross-system user modeling and personalization / Fabian Abel". Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2011. http://d-nb.info/1014252423/34.

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42

Baldiris, Navarro Silvia Margarita. "Supporting competence development processes on open learning systems through personalization". Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/98478.

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This thesis aim for promoting the learning objects economy by offering teacher the possibility of generating adaptive and standardized learning designs. The adaptation of the generated learning design considers two of the most relevant users characteristic: their competences and their learning styles. Standardized and Adaptive Learning Design Generation Process was implemented using HTN planning. Generation process considers a few inputs from the teachers, in particular, those related with the standardized competence definition, the learning objects metadata as well as the data from the initial student model used with adaptation purposes. The learning designs generation process was enriched through the design of two processes, the learning objects searching and positioning processes. These processes permit to look for learning objects in distributed learning objects repositories and use them as inputs for a generated learning design. A layered evaluation process was developed in order to validate the solutions.
El objetivo principal de esta tesis es promover la economía de objetos de aprendizaje ofreciendo al profesor la posibilidad de generar diseños de aprendizaje adaptativos y estandarizados. Los diseños generados consideran dos características de usuario en las adaptaciones: sus competencias y estilos de aprendizaje. El proceso de generación semiautomática de diseños de aprendizaje se implementó utilizando planificación HTN; considerándose pocas entradas por parte del docente, en particular, las definiciones de competencia, los metadatos de los objetos de aprendizaje así como los datos de un modelo inicial del estudiante que serán usados en el proceso de adaptación. El proceso de generación de diseños fue enriquecido a través de dos procesos: los procesos de búsqueda y posicionamiento de objetos de aprendizaje, creados para buscar objetos de aprendizaje en repositorios distribuidos y utilizarlos como entradas para los diseños generados. Se desarrolló un proceso de evaluación por capas para validar las soluciones propuestas.

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43

Deng, Lin. "Mining user preference using SPY voting for search engine personalization /". View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20DENG.

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Keidl, Markus. "Metadata management and context based personalization in distributed information systems". [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973069023.

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Belcher, Kimberlee Neil. "Interactivity and personalization in product presentation on e-commerce websites". Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4262.

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Thesis (M.A.)--University of Missouri-Columbia, 2005.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (November 27, 2006) Includes bibliographical references.

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46

Gopinathan, Sugeeth [Verfasser]. "Personalization and Adaptation in Physical Human-Robot Interaction / Sugeeth Gopinathan". Bielefeld : Universitätsbibliothek Bielefeld, 2019. http://d-nb.info/1181946336/34.

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Lang, Charles WM. "Personalization Through the Application of Inverse Bayes to Student Modeling". Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:16461031.

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Personalization, the idea that teaching can be tailored to each students’ needs, has been a goal for the educational enterprise for at least 2,500 years (Regian, Shute, & Shute, 2013, p.2). Recently personalization has picked up speed with the advent of mobile computing, the Internet and increases in computer processing power. These changes have begun to generate more and more information about individual students that could theoretically be used to power personalized education. The following dissertation discusses a novel algorithm for processing this data to generate estimates of individual level attributes, the Inverse Bayes Filter (IBFi). A brief introduction to the use of Bayes Theorem is followed by a theoretical chapter and then two empirical chapters that describe alternately how the model is constructed, and how it performs on real student data. The theoretical chapter presents both the theory behind Inverse Bayes, including subjective probability, and then relates this theory to student performance. The first empirical chapter describes the prediction accuracy of IBFi on two proxies for students’ subjective probability, partial credit and cumulative average. This prediction performance is compared to the prediction accuracy of a modified Bayesian Knowledge Tracing model (KTPC) with IBFi performing reasonably, out-predicting the KTPC model on a per-student basis but not across all predictions. In the second empirical chapter I validate the theory behind the Inverse Bayes Filter through testing whether student certainty (or confidence) improves prediction performance. The inclusion of student certainty is shown to improve the predictive performance of the model relative to models that do not use certainty. This evidence supports the IBFi model and its underlying theory, indicating that students’ judgments about their levels of certainty in their answers contains information that can be successfully identified by the model. A final summary chapter describes the consequences of using this model for education broadly.
Human Development and Education

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Fernández, Esquer María Eugenia 1957. "TERRITORIAL PERSONALIZATION OF FRONTYARDS IN A MEXICAN PUBLIC HOUSING PROJECT". Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/276482.

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PEPE, GIOVANNI. "Deep Optimization of Discrete Time Filters for Listening Experience Personalization". Doctoral thesis, Università Politecnica delle Marche, 2022. http://hdl.handle.net/11566/293461.

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Questa tesi descrive lo studio di tecniche di Machine Learning per l'ottimizzazione di filtri digitali per l'Equalizzazione Audio Multipunto e la Personal Sound Zones (PSZ) all'interno di uno scenario automotive. L'Equalizzazione Audio Multipunto è un argomento che mira a migliorare la qualità audio in un sistema di altoparlanti utilizzando filtri digitali. La Personal Sound Zones è un task che permette la riproduzione dei suoni in diverse regioni contenute in un ambiente d'ascolto dove sono presenti più ascoltatori. In questa tesi, è stato riportato uno stato dell'arte aggiornato sulla progettazione di filtri digitali, tecniche di Equalizzazione Audio Multipunto e di PSZ. In questa dissertazione, le tecniche di ottimizzazione basate sulle reti neurali, denominate Deep Optimization, hanno dimostrato di essere le più performanti tra i metodi proposti. L'approccio sfrutta le reti neurali per ottimizzare iterativamente i parametri dei filtri utilizzando la feed-forward e la backpropagation e aggiornando i pesi con un ottimizzatore. È stata analizzata una nuova architettura di ottimizzazione profonda, chiamata Bias Network (BiasNet), la quale utilizza i termini di bias come input e aggiorna i suoi pesi per ottenere i filtri ottimali. Gli esperimenti per l'equalizzazione audio con filtri FIR sono stati eseguiti all'interno di vari scenari automotive, ottenendo risultati migliori rispetto alle tecniche presenti nello stato dell'arte. Altri esperimenti sono stati eseguiti con i filtri Parametrici IIR, ottenendo prestazioni migliori rispetto alle tecniche di progettazione dei filtri IIR e FIR. Infine, analizzando il costo computazionale, i filtri IIR Parametrici richiedono meno operazioni e meno memoria. Infine, sono stati condotti esperimenti per progettare filtri FIR e IIR parametrici per PSZ, introducendo termini di regolarizzazione e penalità per eliminare gli artefatti generati dai filtri FIR. I risultati sono molto promettenti, ottenendo un alto contrasto acustico mantenendo una qualità del suono alta. I filtri IIR hanno ottenuto dei risultati comparabili con un costo computazionale inferiore rispetto ai filtri FIR.
This thesis describes the study of Machine Learning techniques for the optimization of digital filters for Multipoint Audio Equalization and Personal Sound Zones (PSZ) in a car scenario. Multipoint Audio Equalization is a topic that aims to improve the audio quality in a loudspeaker system using digital filters. The Personal Sound Zones is a task that allows the reproduction of different sounds in several regions contained within a listening environment where multiple listeners are present. An up-to-date state of the art on digital filter design, Multipoint Audio Equalization and PSZ techniques have been reported in this thesis. Neural network-based optimization techniques, referred to as Deep Optimization, proved to be the best performing and the most analyzed methods within the proposed approaches. The technique exploits neural networks to iteratively optimize the filter parameters using the feed-forward and backpropagation, updating the weights with an optimizer. A new Deep Optimization architecture has been analyzed, called Bias Network (BiasNet), which uses the bias terms as input and updates its weights to obtain the optimal filters. Experiments for Multipoint Audio Equalization with FIR filters were performed within various automotive scenarios, achieving better results than the state-of-the-art techniques. Other experiments were carried out with Parametric IIR filters, achieving better performance than baseline IIR and FIR filter design methods. Furthermore, analyzing the computational cost, Parametric IIR filters require less operations and memory. Finally, experiments were conducted to design FIR and Parametric IIR filters for PSZ, introducing regularization and penalty terms to eliminate artefacts generated by FIR filters. The results are very promising, achieving a high acoustic contrast keeping high sound quality. IIR filters achieved comparable results with a lower computational cost than FIR filters.

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Reviglio, della Venaria Urbano <1990&gt. "Personalization in Social Media: Challenges and Opportunities for Democratic Societies". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amsdottorato.unibo.it/9529/1/Reviglio_Urbano_Tesi.pdf.

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Personalization algorithms perform a fundamental role of knowledge management in order to restrain information overload, reduce complexity and satisfy individuals. Personalization of media content in mainstream social media, however, can be used for micro-target political messages, and can also create filter bubbles and strengthen echo chambers that restrain the exposure to diverse, challenging and serendipitous information. These represent fundamental issues for media law and ethics both seeking to preserve autonomy of choice and media pluralism in democratic societies. As a result, informational empowerment may be reduced and group polarization, audience fragmentation, conspiratorial thinking and other democratically negative consequences could arise. Even though research about the detrimental effects of personalization is more often inconsistent, there is no doubt that in the long run the algorithmic capacity to steer our lives in increasingly sophisticated ways will dramatically expand. Key questions need to be further discussed; for instance, to what extent can profiling account for the complexity of individual identity? To what extent are users, media and algorithms responsible in such practices? What are the main values and trade-offs that inform designers in such a fundamental societal algorithmic arbitrage? How is social media’s personalization directly or indirectly regulated in the European Union? The thesis firstly presents a critical overview of information societies, analyzing social media content personalization practices, dynamics and unintended consequences. Secondly, it explores the role of serendipity as a design and ethical principle for social media. Thirdly, the European legal landscape with regard to personalization is analyzed from a regulatory, governance and ethical perspective. Finally, it is introduced the concept of ‘algorithmic sovereignty’ as a valuable abstraction to begin to frame technical, legal and political preconditions and standards to preserve users’ autonomy, and to minimize the risks arising in the context of personalization.

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