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Academic literature on the topic 'User profile'

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Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "User profile"

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K.R.Ananthapadmanaban, Author, and S. K. Srivatsa. "Personalisation of User Profile: Creating User Profile Ontology for Tamilnadu Tourism." International Journal of Computer Applications 23, no. 8 (June 30, 2011): 42–47. http://dx.doi.org/10.5120/2903-3808.

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Grida, Mohamed, Lamiaa Fayed, and Mohamed Hassan. "User Profile: Theoretical Background." International Journal of Engineering Trends and Technology 68, no. 8 (August 25, 2020): 10–17. http://dx.doi.org/10.14445/22315381/ijett-v68i8p203s.

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CASTELLANO, GIOVANNA, CIRO CASTIELLO, DANILO DELL'AGNELLO, ANNA MARIA FANELLI, CORRADO MENCAR, and MARIA ALESSANDRA TORSELLO. "LEARNING FUZZY USER PROFILES FOR RESOURCE RECOMMENDATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 04 (August 2010): 389–410. http://dx.doi.org/10.1142/s0218488510006611.

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Recommender systems are systems capable of assisting users by quickly providing them with relevant resources according to their interests or preferences. The efficacy of a recommender system is strictly connected with the possibility of creating meaningful user profiles, including information about user preferences, interests, goals, usage data and interactive behavior. In particular, analysis of user preferences is important to predict user behaviors and make appropriate recommendations. In this paper, we present a fuzzy framework to represent, learn and update user profiles. The representation of a user profile is based on a structured model of user cognitive states, including a competence profile, a preference profile and an acquaintance profile. The strategy for deriving and updating profiles is to record the sequence of accessed resources by each user, and to update preference profiles accordingly, so as to suggest similar resources at next user accesses. The adaption of the preference profile is performed continuously, but in earlier stages it is more sensitive to updates (plastic phase) while in later stages it is less sensitive (stable phase) to allow resource recommendation. Simulation results are reported to show the effectiveness of the proposed approach.
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Petersen, Francoise, Giovanni Bartolomeo, and Mike Pluke. "Personalization and User Profile Management." International Journal of Interactive Mobile Technologies (iJIM) 2, no. 4 (September 30, 2008): 25. http://dx.doi.org/10.3991/ijim.v2i4.666.

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Personalization and effective user profile management will be critical to meet the individual usersâ?? needs and for achieving e-Inclusion and e-Accessibility. This paper outlines means to achieve the goal of the new ICT era where services and devices can be personalized by the users in order to meet their needs and preferences, in various situations. Behind every instance of personalization is a profile that stores the user preferences, context of use and other information that can be used to deliver a user experience tailored to their individual needs and preferences. Next Generation Networks (NGN) and the convergence between telephony and Internet services offer a wide range of new terminal and service definition possibilities, and a much wider range of application in society. This paper describes the personalization and profile management activities at European Telecommunications Standards Institute (ETSI) Technical Committee Human Factors, together with relevant experimentations in recent European research projects.
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Ambika, Mani, and K. Latha. "Intelligence Based User Profile Generation." Applied Mechanics and Materials 573 (June 2014): 618–23. http://dx.doi.org/10.4028/www.scientific.net/amm.573.618.

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Web intelligence provides a platform that empowers internet users to determine the most appropriate and best information for their interests. It provides the ability to sense and adapt to the needs and preference of the user. The recent advancements have made it conceivable to capture the users experience and interactions with web. Consequently predicting users behaviors will expedite and enhance browsing experience. This paper proposes an intelligent approach for making the web more powerful by predicting the conduct of individual users. The main goal is to implicitly construct user profiles using a Particle Swarm Optimization - based technique. We reveal interesting results in comparing with a standard user modeling approach.
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Eyharabide, Victoria, and Analía Amandi. "Ontology-based user profile learning." Applied Intelligence 36, no. 4 (June 3, 2011): 857–69. http://dx.doi.org/10.1007/s10489-011-0301-4.

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Zeng, Wenjing, Rui Tang, Haizhou Wang, Xingshu Chen, and Wenxian Wang. "User Identification Based on Integrating Multiple User Information across Online Social Networks." Security and Communication Networks 2021 (May 25, 2021): 1–14. http://dx.doi.org/10.1155/2021/5533417.

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User identification can help us build more comprehensive user information. It has been attracting much attention from academia. Most of the existing works are profile-based user identification and relationship-based user identification. Due to user privacy settings and social network restrictions on user data crawl, user data may be missing or incomplete in real social networks. User data include profiles, user-generated contents (UGCs), and relationships. The features extracted in previous research may be sparse. In order to reduce the impact of the above problems on user identification, we propose a multiple user information user identification framework (MUIUI). Firstly, we develop multiprocess crawlers to obtain the user data from two popular social networks, Twitter and Facebook. Secondly, we use named entity recognition and entity linking to obtain and integrate locations and organizations from profiles and UGCs. We also extract URLs from profiles and UGCs. We apply the locations jointly with the relationships and develop several algorithms to measure the similarity of the display name, all locations, all organizations, location in profile, all URLs, following organizations, and user ID, respectively. Afterward, we propose a fusion classifier machine learning-based user identification method. The results show that the F1 score of MUIUI reaches 86.46% on the dataset. It proves that MUIUI can reduce the impact of user data that are missing or incomplete.
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Iggui, Taous, Hassina Nacer, Youcef Sklab, and Taklit Ait Radi. "Web Application for User Profiling." International Journal of Information Systems in the Service Sector 8, no. 2 (April 2016): 44–56. http://dx.doi.org/10.4018/ijisss.2016040104.

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User's profiles play an important role when information systems try to meet their needs. This work presents a novel approach to build user profiles. It is based on information extraction techniques and proceeds by iterative steps. The use of different statistic metrics, Natural Language Processing (NLP) techniques and semantic descriptions (ontologies) in the authors' approach, has provided it with a good precision degree when extracting information from texts. This has been demonstrated by an application prototype which is an automatic user profile constructor, using the texts of emails job applications (E recruitment field).
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Tamboli, Najneen, and Sathish Kumar Penchala. "User Profile Based Personalized Web Search." International Journal of Managing Public Sector Information and Communication Technologies 7, no. 3 (September 30, 2016): 15–22. http://dx.doi.org/10.5121/ijmpict.2016.7302.

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Kaya, Buket. "User Profile Based Paper Recommendation System." International Journal of Intelligent Systems and Applications in Engineering 2, no. 6 (June 29, 2018): 151–57. http://dx.doi.org/10.18201/ijisae.2018642079.

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Dissertations / Theses on the topic "User profile"

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Boc, Michaël Mathias. "Profile of mobility : user-centric networking." Paris 6, 2009. http://www.theses.fr/2009PA066355.

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Limam, Lyes. "Usage-driven unified model for user profile and data source profile extraction." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0058/document.

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La problématique traitée dans la thèse s’inscrit dans le cadre de l’analyse d’usage dans les systèmes de recherche d’information. En effet, nous nous intéressons à l’utilisateur à travers l’historique de ses requêtes, utilisées comme support d’analyse pour l’extraction d'un profil d’usage. L’objectif est de caractériser l’utilisateur et les sources de données qui interagissent dans un réseau afin de permettre des comparaisons utilisateur-utilisateur, source-source et source-utilisateur. Selon une étude que nous avons menée sur les travaux existants sur les modèles de profilage, nous avons conclu que la grande majorité des contributions sont fortement liés aux applications dans lesquelles ils étaient proposés. En conséquence, les modèles de profils proposés ne sont pas réutilisables et présentent plusieurs faiblesses. Par exemple, ces modèles ne tiennent pas compte de la source de données, ils ne sont pas dotés de mécanismes de traitement sémantique et ils ne tiennent pas compte du passage à l’échelle (en termes de complexité). C'est pourquoi, nous proposons dans cette thèse un modèle d’utilisateur et de source de données basé sur l’analyse d’usage. Les caractéristiques de ce modèle sont les suivantes. Premièrement, il est générique, permettant de représenter à la fois un utilisateur et une source de données. Deuxièmement, il permet de construire le profil de manière implicite à partir de l’historique de requêtes de recherche. Troisièmement, il définit le profil comme un ensemble de centres d’intérêts, chaque intérêt correspondant à un cluster sémantique de mots-clés déterminé par un algorithme de clustering spécifique. Et enfin, dans ce modèle le profil est représenté dans un espace vectoriel. Les différents composants du modèle sont organisés sous la forme d’un Framework, la complexité de chaque composant y est évaluée. Le Framework propose : - une méthode pour la désambigüisation de requêtes; - une méthode pour la représentation sémantique des logs sous la forme d’une taxonomie ; - un algorithme de clustering qui permet l’identification rapide et efficace des centres d’intérêt représentés par des clusters sémantiques de mots clés ; - une méthode pour le calcul du profil de l’utilisateur et du profil de la source de données à partir du modèle générique. Le Framework proposé permet d'effectuer différentes tâches liées à la structuration d’un environnement distribué d’un point de vue usage. Comme exemples d’application, le Framework est utilisé pour la découverte de communautés d’utilisateurs et la catégorisation de sources de données. Pour la validation du Framework, une série d’expérimentations est menée en utilisant des logs du moteur de recherche AOL-search, qui ont démontrées l’efficacité de la désambigüisation sur des requêtes courtes, et qui ont permis d’identification de la relation entre le clustering basé sur une fonction de qualité et le clustering basé sur la structure
This thesis addresses a problem related to usage analysis in information retrieval systems. Indeed, we exploit the history of search queries as support of analysis to extract a profile model. The objective is to characterize the user and the data source that interact in a system to allow different types of comparison (user-to-user, source-to-source, user-to-source). According to the study we conducted on the work done on profile model, we concluded that the large majority of the contributions are strongly related to the applications within they are proposed. As a result, the proposed profile models are not reusable and suffer from several weaknesses. For instance, these models do not consider the data source, they lack of semantic mechanisms and they do not deal with scalability (in terms of complexity). Therefore, we propose a generic model of user and data source profiles. The characteristics of this model are the following. First, it is generic, being able to represent both the user and the data source. Second, it enables to construct the profiles in an implicit way based on histories of search queries. Third, it defines the profile as a set of topics of interest, each topic corresponding to a semantic cluster of keywords extracted by a specific clustering algorithm. Finally, the profile is represented according to the vector space model. The model is composed of several components organized in the form of a framework, in which we assessed the complexity of each component. The main components of the framework are: - a method for keyword queries disambiguation; - a method for semantically representing search query logs in the form of a taxonomy; - a clustering algorithm that allows fast and efficient identification of topics of interest as semantic clusters of keywords; - a method to identify user and data source profiles according to the generic model. This framework enables in particular to perform various tasks related to usage-based structuration of a distributed environment. As an example of application, the framework is used to the discovery of user communities, and the categorization of data sources. To validate the proposed framework, we conduct a series of experiments on real logs from the search engine AOL search, which demonstrate the efficiency of the disambiguation method in short queries, and show the relation between the quality based clustering and the structure based clustering
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Johansson, Ida, and Stina Johansson. "Users from a role perspective : A model to provide a user-centered system development process." Thesis, Linköping University, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8925.

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Today there exist several systems that lack usability. The reasons are, among others, the pressure to add more features in the system, but also that focus has been earlier on technology and functionality. It becomes more important to think of the user, so that systems can be more usable. By focusing on the user, the user experience can enhance, which results in more satisfied users.

User involvement in the system development process is one way to learn and receive information about the user. There are several ways of doing that. One way is to create user portraits from existing user models. We have studied different user models and lack a model that includes both tasks and goals; for example one of the existing models focus on user goals and another on relations with the system.

The purpose with this final thesis is to create a model to describe users as roles providing a more effective and generalized user-centered system development process.

To create this model, the role profile model, we studied existing theories. By them we were influenced both by good and less good things. The theoretical study resulted in a tentative model that we tested by interview and observe six buyers within the manufacturing industry. This resulted in two role profiles; the strategic buyer and purchase planner. From the result of the interviews and observations, we improved the tentative role profile model to our final model. There were some factors that did not fit in the tentative model and some that we added.

The role profile model contains three elements; objectives, requirements and context. These elements consist of a number of attributes that are important when surveying a role profile. The uniqueness with the role profile model is the fact that it includes both role specific attributes and attributes that puts the role profile into a context. The model contains for example work process, system requirements, responsibility and authority.

The role profile is at part of a greater context. There are design philosophies that concern the whole system development process; from survey the user to design and test. Our role profile model only handles the first parts, the user and tasks, due to the time limitations.

We have created a practical procedure that we recommend to facilitate the usage of the role profile model. The practical procedure is based on our choices of method when testing the tentative role profile model. The central in our practical procedure are interviews and observations. It is important to plan the company visits so that all necessary information is received.

The usage of the role profile can for example contribute with: knowledge and understanding of the users, structured work procedure, a generally accepted terminology and a user-centered design process.

This final thesis has been performed on commission of Lawson Software. We had continuously discussions with them about usability and the role profile model. We have studied fields of applications for the role profile model at Lawson. We think they can use the model in the product requirement process, design and evaluation, work with composite applications, user understanding and user priority and introduction of new employees.


Idag finns det ett flertal system som har bristande användar¬vänlighet. Detta beror bland annat på påtryckningar att lägga till fler funktioner i systemen, men också på att det bakåt i tiden har fokuserats på att utveckla system utifrån teknologi och funktionalitet. Det blir allt viktigare att tänka på användaren så att systemen blir mer användarvänliga. Genom att fokusera på användaren kan man öka användarupplevelsen i systemet och det medför att användarna blir nöjdare.

Att involvera användaren i utvecklingsprocessen är ett sätt att lära känna och få information om användaren. Det finns många sätt att göra det på. Ett sätt är att skapa användarporträtt med hjälp av olika befintliga användarmodeller. Vi har studerat olika användarmodeller och sett att det saknas en användarmodell som inkluderar både fokus på användarens arbetsprocess och dess mål; till exempel en av de existerande modellerna fokuserar på användarens mål och en annan på relationer med system.

Syftet med detta examensarbete är därför att skapa en modell som beskriver användare som roller, som bidrar till en effektivare och bredare användar¬centrerad systemutvecklingsprocess.

För att ta fram denna modell, rollprofilsmodellen, studerade vi som sagt existerande teorier. Från dem påverkades vi både av delar som vi tyckte var bra och mindre bra. Vi kom fram till en hypotesmodell som vi testade genom att intervjua och observera sex inköpare på tillverkande företag. Detta resulterade i två rollprofiler; den strategiska inköparen och inköpsplaneraren. Utifrån resultaten av intervjuerna och observationerna förbättrade vi hypotesen till vår slutliga modell. Det fanns vissa faktorer som vi inte tyckte passade in, men även attribut som borde läggas till.

Rollprofilsmodellen innehåller tre delar; mål, krav och kontext. Dessa delar består av ett antal attribut som är viktiga att undersöka hos den rollprofil som ska skapas. Det som utmärker rollprofilsmodellen är att den inkluderar både roll¬speci¬fika mål och attribut som sätter rollprofilen i en kontext. Modellen innehåller till exempel relationer, krav, ansvar och befogenheter.

Rollprofilen är en del av en större kontext. Det finns designfilosofier som berör hela systemutvecklingsprocessen; från att kartlägga användaren till att designa och testa. På grund av tidsbegränsningar har vi enbart berört de första av dessa delar; användaren och arbetsuppgifter.

För att underlätta användandet av rollprofilen har vi skapat ett rekommenderat praktiskt tillvägagångssätt. Detta är baserat på våra egna metodval när vi testade vår hypotesmodell. Det centrala i det praktiska tillvägagångssättet är intervjuer och observa¬tioner. Det är viktigt att planera noggrant så att all nödvändig informa¬tion erhålls.

Användandet av rollprofilen kan till exempel bidra till: kunskap och förståelse av användaren, ett strukturerat arbetssätt, en allmänt accepterad terminologi samt en användar¬centrerad utvecklingsprocess.

Detta examensarbete har utförts på uppdrag av Lawson Software, med vilka vi kontinuer¬ligt har fört diskussioner om användbarhet och rollprofilsmodellen med mera. Vi har även utforskat vilka potentiella användningsområden som rollprofilen har på Lawson. Lawson skulle kunna använda modellen i kravhanteringsprocessen, design och utvärdering, arbete med komposita applikationer, användarförståelse och priori¬teringar samt intro¬ducerande av nyanställda.

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Oelofse, Christina Hermiena. "The learner profile of a teenage cell phone user." Diss., Pretoria : [s.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-07102009-160407.

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Kacem, Sahraoui Ameni. "Personalized information retrieval based on time-sensitive user profile." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30111/document.

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Les moteurs de recherche, largement utilisés dans différents domaines, sont devenus la principale source d'information pour de nombreux utilisateurs. Cependant, les Systèmes de Recherche d'Information (SRI) font face à de nouveaux défis liés à la croissance et à la diversité des données disponibles. Un SRI analyse la requête soumise par l'utilisateur et explore des collections de données de nature non structurée ou semi-structurée (par exemple : texte, image, vidéo, page Web, etc.) afin de fournir des résultats qui correspondent le mieux à son intention et ses intérêts. Afin d'atteindre cet objectif, au lieu de prendre en considération l'appariement requête-document uniquement, les SRI s'intéressent aussi au contexte de l'utilisateur. En effet, le profil utilisateur a été considéré dans la littérature comme l'élément contextuel le plus important permettant d'améliorer la pertinence de la recherche. Il est intégré dans le processus de recherche d'information afin d'améliorer l'expérience utilisateur en recherchant des informations spécifiques. Comme le facteur temps a gagné beaucoup d'importance ces dernières années, la dynamique temporelle est introduite pour étudier l'évolution du profil utilisateur qui consiste principalement à saisir les changements du comportement, des intérêts et des préférences de l'utilisateur en fonction du temps et à actualiser le profil en conséquence. Les travaux antérieurs ont distingué deux types de profils utilisateurs : les profils à court-terme et ceux à long-terme. Le premier type de profil est limité aux intérêts liés aux activités actuelles de l'utilisateur tandis que le second représente les intérêts persistants de l'utilisateur extraits de ses activités antérieures tout en excluant les intérêts récents. Toutefois, pour les utilisateurs qui ne sont pas très actifs dont les activités sont peu nombreuses et séparées dans le temps, le profil à court-terme peut éliminer des résultats pertinents qui sont davantage liés à leurs intérêts personnels. Pour les utilisateurs qui sont très actifs, l'agrégation des activités récentes sans ignorer les intérêts anciens serait très intéressante parce que ce type de profil est généralement en évolution au fil du temps. Contrairement à ces approches, nous proposons, dans cette thèse, un profil utilisateur générique et sensible au temps qui est implicitement construit comme un vecteur de termes pondérés afin de trouver un compromis en unifiant les intérêts récents et anciens. Les informations du profil utilisateur peuvent être extraites à partir de sources multiples. Parmi les méthodes les plus prometteuses, nous proposons d'utiliser, d'une part, l'historique de recherche, et d'autre part les médias sociaux
Recently, search engines have become the main source of information for many users and have been widely used in different fields. However, Information Retrieval Systems (IRS) face new challenges due to the growth and diversity of available data. An IRS analyses the query submitted by the user and explores collections of data with unstructured or semi-structured nature (e.g. text, image, video, Web page etc.) in order to deliver items that best match his/her intent and interests. In order to achieve this goal, we have moved from considering the query-document matching to consider the user context. In fact, the user profile has been considered, in the literature, as the most important contextual element which can improve the accuracy of the search. It is integrated in the process of information retrieval in order to improve the user experience while searching for specific information. As time factor has gained increasing importance in recent years, the temporal dynamics are introduced to study the user profile evolution that consists mainly in capturing the changes of the user behavior, interests and preferences, and updating the profile accordingly. Prior work used to discern short-term and long-term profiles. The first profile type is limited to interests related to the user's current activities while the second one represents user's persisting interests extracted from his prior activities excluding the current ones. However, for users who are not very active, the short-term profile can eliminate relevant results which are more related to their personal interests. This is because their activities are few and separated over time. For users who are very active, the aggregation of recent activities without ignoring the old interests would be very interesting because this kind of profile is usually changing over time. Unlike those approaches, we propose, in this thesis, a generic time-sensitive user profile that is implicitly constructed as a vector of weighted terms in order to find a trade-off by unifying both current and recurrent interests. User profile information can be extracted from multiple sources. Among the most promising ones, we propose to use, on the one hand, searching history. Data from searching history can be extracted implicitly without any effort from the user and includes issued queries, their corresponding results, reformulated queries and click-through data that has relevance feedback potential. On the other hand, the popularity of Social Media makes it as an invaluable source of data used by users to express, share and mark as favorite the content that interests them
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Nadee, Wanvimol. "Modelling user profiles for recommender systems." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93723/1/Wanvimol_Nadee_Thesis.pdf.

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Recommender systems assist users in finding what they want. The challenging issue is how to efficiently acquire user preferences or user information needs for building personalized recommender systems. This research explores the acquisition of user preferences using data taxonomy information to enhance personalized recommendations for alleviating cold-start problem. A concept hierarchy model is proposed, which provides a two-dimensional hierarchy for acquiring user preferences. The language model is also extended for the proposed hierarchy in order to generate an effective recommender algorithm. Both Amazon.com book and music datasets are used to evaluate the proposed approach, and the experimental results show that the proposed approach is promising.
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Mahmood, Omer. "ADAPTIVE PROFILE DRIVEN DATA CACHING AND PREFETCHING IN MOBILE ENVIRONMENT." Thesis, The University of Sydney, 2005. http://hdl.handle.net/2123/714.

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This thesis describes a new method of calculating data priority by using adaptive mobile user and device profiles which change with user location, time of the day, available networks and data access history. The profiles are used for data prefetching, selection of most suitable wireless network and cache management on the mobile device in order to optimally utilize the device's storage capacity and available bandwidth. Some of the inherent characteristics of mobile devices due to user movements are – non-persistent connection, limited bandwidth and storage capacity, changes in mobile device's geographical location and connection (eg. connection can be from GPRS to WLAN to Bluetooth). New research is being carried out in making mobile devices work more efficiently by reducing and/or eliminating their limitations. The focus of this research is to propose, evaluate and test a new user profiling technique which specifically caters to the needs of the mobile device users who are required to access large amounts of data, possibly more than the device storage capability during the course of the day or week. This work involves the development of an intelligent user profiling system along with mobile device caching system which will first allocate weight (priority) to the different sets and subsets of the total given data based on user's location, user's appointment information, user's preferences, device capabilities and available networks. Then the profile will automatically change the data weights with user movements, history of cached data access and characteristics of available networks. The Adaptive User and Device Profiles were designed to handle broad range of the issues associated with: •Changing network types and conditions •Limited storage capacity and document type support of mobile devices •Changes in user data needs due to their movements at different times of the day Many research areas have been addressed through this research but the primary focus has remained on the following four core areas. The four core areas are : selecting the most suitable wireless network; allocating weights to different datasets & subsets by integrating user's movements; previously accessed data; time of the day with user appointment information and device capabilities.
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Mahmood, Omer. "ADAPTIVE PROFILE DRIVEN DATA CACHING AND PREFETCHING IN MOBILE ENVIRONMENT." University of Sydney. Information Technologies, 2005. http://hdl.handle.net/2123/714.

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This thesis describes a new method of calculating data priority by using adaptive mobile user and device profiles which change with user location, time of the day, available networks and data access history. The profiles are used for data prefetching, selection of most suitable wireless network and cache management on the mobile device in order to optimally utilize the device�s storage capacity and available bandwidth. Some of the inherent characteristics of mobile devices due to user movements are �non-persistent connection, limited bandwidth and storage capacity, changes in mobile device�s geographical location and connection (eg. connection can be from GPRS to WLAN to Bluetooth). New research is being carried out in making mobile devices work more efficiently by reducing and/or eliminating their limitations. The focus of this research is to propose, evaluate and test a new user profiling technique which specifically caters to the needs of the mobile device users who are required to access large amounts of data, possibly more than the device storage capability during the course of the day or week. This work involves the development of an intelligent user profiling system along with mobile device caching system which will first allocate weight (priority) to the different sets and subsets of the total given data based on user�s location, user�s appointment information, user�s preferences, device capabilities and available networks. Then the profile will automatically change the data weights with user movements, history of cached data access and characteristics of available networks. The Adaptive User and Device Profiles were designed to handle broad range of the issues associated with: �Changing network types and conditions �Limited storage capacity and document type support of mobile devices �Changes in user data needs due to their movements at different times of the day Many research areas have been addressed through this research but the primary focus has remained on the following four core areas. The four core areas are : selecting the most suitable wireless network; allocating weights to different datasets & subsets by integrating user�s movements; previously accessed data; time of the day with user appointment information and device capabilities.
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Lal, Neeraj. "Enhancing Location-Based Content Delivery Through Semi-Automated Generation of User Profile." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1293731843.

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Chukala, Sileshi. "A Move Towards Practicability : Context Information and User Profile Management for the SensibleThings Platform." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29365.

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Achievements in academia and industry indicate a move towards the creation of a giant smart system including objects of different sizes, shapes, and purposes where the Internet will be so diverse and more resourceful with the incorporation of different smart devices. Mid Swe-den University took a first step towards providing an appropriate plat-form for Internet of Things applications known as SensibleThings for the emerging intercommunication of “objects”, called Internet of Things. The SensibleThings platform has produced components for sharing sensor-based context information on the Internet, in order to enable Internet of Things applications. In this thesis the plan was to contribute to filling the gap between the IoT and developers in academia as well as in the industries. The aim of the project was to create basic functionality to enable users to find new interesting users on the IoT and prioritize existing users. To this end context information and user profile man-agement is proposed and presented. Furthermore, a comparison be-tween the platform with the proposed extension as well as without was compared and examined.
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Books on the topic "User profile"

1

Profile, FT, ed. FT Profile Business Information user manual. Sunbury on Thames: FT Profile, 1991.

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Computer Systems Laboratory (U.S.), ed. The user interface component of the applications portability profile. Gaithersburg, MD: Computer Systems Laboratory, National Institute of Standards and Technology, 1994.

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David, Saunders, and Ames Research Center, eds. PROFILE: Airfoil geometry manipulation and display : user's guide. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1997.

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George C. Marshall Space Flight Center., ed. User's guide for monthly vector wind profile model. [Marshall Space Flight Center], Ala: National Aeronautics and Space Administration, Marshall Space Flight Center, 1999.

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Clift, Laurence. Ergonomics evaluation into the safety of stepladders: User profile and dynamic testing phase 2. [Sudbury]: HSE, 2002.

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Batjes, N. H. World inventory of soil emission potentials, Wise 2.1: Profile database user manual and coding protocols. Wageningen, the Netherlands: International Soil Reference and Information Centre, 1995.

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Boland, Tim. Government Open Systems Interconnection Profile users' guide. Gaithersburg, MD: U.S. Dept. of Commerce, National Institute of Standards and Technology, 1989.

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Boland, Tim. Government open systems interconnection profile users' guide. Washington, D.C: National Institute of Standards and Technology, 1989.

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Boland, Tim. Government open systems interconnection profile users' guide. Gaithersburg, MD: U.S. Dept. of Commerce, National Institute of Standards and Technology, 1989.

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Moller, Rosa Maria. Profile of California computer and internet users. Sacramento, CA: California State Library, California Research Bureau, 2000.

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Book chapters on the topic "User profile"

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Weik, Martin H. "user profile." In Computer Science and Communications Dictionary, 1875. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_20606.

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Weik, Martin H. "station user profile." In Computer Science and Communications Dictionary, 1662. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_18206.

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Weik, Martin H. "workstation user profile." In Computer Science and Communications Dictionary, 1932. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_21222.

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Weik, Martin H. "operator-user profile." In Computer Science and Communications Dictionary, 1157. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_12916.

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Weik, Martin H. "system operator-user profile." In Computer Science and Communications Dictionary, 1722. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_18910.

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Catrinescu, Vlad, and Trevor Seward. "Configuring the User Profile Service." In Deploying SharePoint 2016, 141–61. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1999-7_7.

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Catrinescu, Vlad, and Trevor Seward. "Configuring the User Profile Service." In Deploying SharePoint 2019, 187–211. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4526-2_7.

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Kuflik, Tsvi, Bracha Shapira, Yuval Elovici, and Adlai Maschiach. "Privacy Preservation Improvement by Learning Optimal Profile Generation Rate." In User Modeling 2003, 168–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44963-9_23.

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Kumar, Harshit, and Hong-Gee Kim. "Using Folksonomies for Building User Interest Profile." In User Modeling, Adaption and Personalization, 438–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22362-4_46.

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Kamba, Tomonari, and Laurent Freléchoux. "Where to locate user profiles of personalized applications? — A user profile management agent —." In Human-Computer Interaction INTERACT ’97, 555–62. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-0-387-35175-9_83.

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Conference papers on the topic "User profile"

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Gatziolis, Kleanthis, and Anthony C. Boucouvalas. "User profile extraction engine." In PCI '16: 20th Pan-Hellenic Conference on Informatics. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3003733.3003761.

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Chen Yu, Yu Yang, Zhang Wei, and Shen Junyi. "Analyzing User Behavior History for constructing user profile." In 2008 IEEE International Symposium on IT in Medicine and Education (ITME). IEEE, 2008. http://dx.doi.org/10.1109/itme.2008.4743884.

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Ouaftouh, Sara, Ahmed Zellou, and Ali Idri. "User profile model: A user dimension based classification." In 2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE, 2015. http://dx.doi.org/10.1109/sita.2015.7358378.

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Tripathy, Amiya Kumar, and Royston Olivera. "UProRevs-User Profile Relevant Results." In 10th International Conference on Information Technology (ICIT 2007). IEEE, 2007. http://dx.doi.org/10.1109/icit.2007.39.

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Flores, Marcel, and Aleksandar Kuzmanovic. "Synthoid: Endpoint User Profile Control." In 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2014. http://dx.doi.org/10.1109/wi-iat.2014.104.

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Xu, Yuyang, and Bing Li. "Sentiment Classification Incorporating User Profile." In 2017 4th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2017. http://dx.doi.org/10.1109/icisce.2017.144.

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Wischenbart, Martin, Stephan Lechner, Stefan Mitsch, Elisabeth Kapsammer, Angelika Kusel, Birgit Pröll, Werner Retschitzegger, Wieland Schwinger, Johannes Schönböck, and Manuel Wimmer. "User profile integration made easy." In the 21st international conference companion. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2187980.2188227.

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Tripathy, Amiya Kumar, and Royston Olivera. "UProRevs-User Profile Relevant Results." In 10th International Conference on Information Technology (ICIT 2007). IEEE, 2007. http://dx.doi.org/10.1109/icoit.2007.4418311.

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Park, Sung-Hyuk, Ho-Jin Lee, Sang-Pil Han, and Dong-Hoon Lee. "User Age Profile Assessment Using SMS Network Neighbors' Age Profiles." In 2009 International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, 2009. http://dx.doi.org/10.1109/waina.2009.136.

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Labriji, Amine, Salma Charkaoui, Issam Abdelbaki, and Abdelwahed Namir. "User interest center based on a semantic user profile." In 2016 5th International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2016. http://dx.doi.org/10.1109/icmcs.2016.7905556.

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Reports on the topic "User profile"

1

Tessin, T. SPI/UNIX user manual: Security Profile Inspector. Office of Scientific and Technical Information (OSTI), November 1989. http://dx.doi.org/10.2172/6845654.

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Zeilenga, K. SASLprep: Stringprep Profile for User Names and Passwords. RFC Editor, February 2005. http://dx.doi.org/10.17487/rfc4013.

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Hilt, V., G. Camarillo, J. Rosenberg, and D. Worley. A User Agent Profile Data Set for Media Policy. RFC Editor, December 2012. http://dx.doi.org/10.17487/rfc6796.

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Petrie, D. A Framework for Session Initiation Protocol User Agent Profile Delivery. Edited by S. Channabasappa. RFC Editor, March 2011. http://dx.doi.org/10.17487/rfc6080.

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Bocci, M., L. Levrau, and D. Frost. MPLS Transport Profile User-to-Network and Network-to-Network Interfaces. RFC Editor, April 2011. http://dx.doi.org/10.17487/rfc6215.

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Witherspoon, T. T. FY 1997 Hanford telecommunication and informations system user profile, milestone IRM-097-003. Office of Scientific and Technical Information (OSTI), September 1997. http://dx.doi.org/10.2172/325205.

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Clement, Michael. Engineering With Nature website user guide. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43440.

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The Engineering With Nature (EWN) program is a high-profile effort that aims to deliver cost-effective, broadly beneficial solutions to natural re-source and sustainability challenges across the nation. A portion of this is accomplished through the use of the EWN website, which features news, podcasts, articles, and more. The content on the EWN website serves to educate and inform hundreds of visitors monthly. This content is generated and managed by EWN team members with web development experience, as it requires manually editing the website HTML and staging changes on a development server. With the EWN website 2.0, a new website framework (WordPress) has been implemented that will save content managers time and effort by providing a front-end user interface (UI) to enable the uploading, staging, and approval of new content for the website, along with a visual refresh to herald the impending release of season 2 of the EWN Podcast. This document’s purpose is to demonstrate the functionality of the new EWN website and provide instructional material for those managing content via the new EWN website.
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Dubeck, Margaret M., Jonathan M. B. Stern, and Rehemah Nabacwa. Learning to Read in a Local Language in Uganda: Creating Learner Profiles to Track Progress and Guide Instruction Using Early Grade Reading Assessment Results. RTI Press, June 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0068.2106.

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The Early Grade Reading Assessment (EGRA) is used to evaluate studies and monitor projects that address reading skills in low- and middle-income countries. Results are often described solely in terms of a passage-reading subtask, thereby overlooking progress in related skills. Using archival data of cohort samples from Uganda at two time points in three languages (Ganda, Lango, and Runyankore-Rukiga), we explored a methodology that uses passage-reading results to create five learner profiles: Nonreader, Beginner, Instructional, Fluent, and Next-Level Ready. We compared learner profiles with results on other subtasks to identify the skills students would need to develop to progress from one profile to another. We then used regression models to determine whether students’ learner profiles were related to their results on the various subtasks. We found membership in four categories. We also found a shift in the distribution of learner profiles from Grade 1 to Grade 4, which is useful for establishing program effectiveness. The distribution of profiles within grades expanded as students progressed through the early elementary grades. We recommend that those who are discussing EGRA results describe students by profiles and by the numbers that shift from one profile to another over time. Doing so would help describe abilities and instructional needs and would show changes in a meaningful way.
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Kochner, Kathryn M., and George W. Thomas. USAR Enlisted Loss Profiles. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada239108.

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Carlson, Jake. The Data Curation Profiles Toolkit User Guide. Purdue University Libraries / Distributed Data Curation Center, November 2010. http://dx.doi.org/10.5703/1288284315650.

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