Dissertations / Theses on the topic 'Personalization'
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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.
Full textAlmerfors, 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.
Full textAsif, 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.
Full textDonnelly, Christopher. "Enhancing Personalization Within ASSISTments." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/249.
Full textYang, Yanwu. "Towards spatial web personalization." Paris, ENSAM, 2006. https://pastel.archives-ouvertes.fr/pastel-00002481.
Full textIn 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
Boutet, Antoine. "Decentralizing news personalization systems." Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S023/document.
Full textThe 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
Hoang, Van Tieng. "Measuring Web Search Personalization." Thesis, IMT Alti Studi Lucca, 2018. http://e-theses.imtlucca.it/246/1/Hoang_phdthesis.pdf.
Full textSONG, Songbo. "Advanced personalization of IPTV services." Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00814620.
Full textSong, 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.
Full textCataloged 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
Li, Andrew A. (Andrew Andi). "Algorithms for large-scale personalization." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119351.
Full textCataloged 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.
Perugini, Saverio. "Program Transformations for Information Personalization." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11196.
Full textPh. D.
Song, Songbo. "Advanced personalization of IPTV services." Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0001/document.
Full textInternet 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
Song, Songbo. "Advanced personalization of IPTV services." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0001.
Full textInternet 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
Schilke, Steffen Walter. "Multi-dimensional-personalization in mobile contexts." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1576.
Full textAtwi, Aliaa. "Exploration vs. exploitation in coupon personalization." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115729.
Full textCataloged 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
Violante, João G. "Behavior-based personalization : strategies and Implications." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65820.
Full textCataloged 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.
Sah, Melike. "Semantic linking and personalization in context." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/66605/.
Full textPaiu, Raluca. "Exploiting tag information for search and personalization." Hannover Technische Informationsbibliothek und Universitätsbibliothek Hannover, 2010. http://d-nb.info/1001050592/34.
Full textFriberg, 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.
Full textDetta 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.
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.
Full textTeevan, 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.
Full textThis 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.
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.
Full textCurrently, 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.
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.
Full textWe 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.
Rashad, Hisham S. M. "Trust Management Systems: Reference Architecture and Personalization." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/88457.
Full textPHD
Pfiffelmann, Jean. "Identification-based personalization effects in recruitment advertising." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSE3026.
Full textAttracting 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
Döring, Sven. "Personalization of the search process in tourism." kostenfrei, 2008. http://d-nb.info/989393860/34.
Full textAnderson, Corin R. "A machine learning approach to Web personalization /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6875.
Full textHaraty, Mona. "Designing for authoring and sharing of advanced personalization." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59508.
Full textScience, Faculty of
Computer Science, Department of
Graduate
Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.
Full textCan 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
Cloarec, Julien. "The Personalization-Privacy Paradox in the Attention Economy." Thesis, Toulouse 1, 2019. http://www.theses.fr/2019TOU10049.
Full textThe 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)
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.
Full textDiliwi, Avesta, Christopher Ullberg, and 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.
Full textPaiu, Raluca [Verfasser]. "Exploiting tag information for search and personalization / Raluca Paiu." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover, 2010. http://d-nb.info/1001050592/34.
Full textRudinski, 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.
Full textVu, 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.
Full textFlory, Long Mrs. "A WEB PERSONALIZATION ARTIFACT FOR UTILITY-SENSITIVE REVIEW ANALYSIS." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3739.
Full textSadasivam, 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.
Full textTitle 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).
Garrigós, Irene. "A-OOH: extending web application design with dynamic personalization." Doctoral thesis, Universidad de Alicante, 2008. http://hdl.handle.net/10045/9645.
Full textFERRARI, 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.
Full textRecognizing 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.[...]
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.
Full textAbel, 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.
Full textBaldiris, 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.
Full textEl 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.
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.
Full textKeidl, 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.
Full textBelcher, 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.
Full textThe 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.
Gopinathan, Sugeeth [Verfasser]. "Personalization and Adaptation in Physical Human-Robot Interaction / Sugeeth Gopinathan." Bielefeld : Universitätsbibliothek Bielefeld, 2019. http://d-nb.info/1181946336/34.
Full textLang, 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.
Full textHuman Development and Education
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.
Full textPEPE, GIOVANNI. "Deep Optimization of Discrete Time Filters for Listening Experience Personalization." Doctoral thesis, Università Politecnica delle Marche, 2022. http://hdl.handle.net/11566/293461.
Full textThis 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.
Reviglio, della Venaria Urbano <1990>. "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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