BibSonomyburst/user/brusilovsky/user-profileBibSonomy RSS feed for /user/brusilovsky/user-profile2012-02-17T01:35:59+01:00http://www.bibsonomy.org/bibtex/208a0d0bcbf1042ce668bf6b5176cb305/brusilovskybrusilovsky2009-10-01T12:25:50+02:00ectel09 social-network user-profile <span class="authorEditorList"><a href="/author/Wodzicki">Katrin Wodzicki</a>, <a href="/author/Schwaemmlein">Eva Schwaemmlein</a>, and <a href="/author/Cress">Ulrike Cress</a> </span><em>Learning in the Synergy of Multiple Disciplines, Proceedings of the EC-TEL 2009, </em><em>volume 5794 of Lecture Notes in Computer Science, </em><em>Berlin/Heidelberg, </em><em>Springer, </em>(<em>October 2009</em>)Thu Oct 01 12:25:50 CEST 2009Berlin/HeidelbergLearning in the Synergy of Multiple Disciplines, Proceedings of the EC-TEL 2009OctoberLecture Notes in Computer ScienceHow to Get Proper Profiles? Psychological Perspective on Social Networking Sites57942009ectel09 social-network user-profile Research on transactive memory systems has shown the importance of knowledge about who knows what. Going beyond the issue of work group settings, the article underlines the importance of such knowledge for finding one's way through today's knowledge society. We discuss how social networking sites can be used to manage individual knowledge networks. Therefore, we describe characteristics of social networking sites resulting in the conclusion that user profiles serve as a base for an external transactive memory system. Furthermore, we draw attention to the perspective of the individual users and the functions that user profiles have for them. This leads to guiding propositions how to improve the implementation of user profiles for establishing and maintaining an useful external transactive memory system.http://www.bibsonomy.org/bibtex/236f22782ea66f1cb951faea20690cf9c/brusilovskybrusilovsky2009-07-01T11:12:30+02:00adaptive-search adaptive-web user-profile <span class="authorEditorList"><a href="/author/Tanudjaja">F. Tanudjaja</a>, and <a href="/author/Mui">L. Mui</a> </span>(<em>2002</em>)Wed Jul 01 11:12:30 CEST 2009Persona: A contextualized and personalized Web search2002adaptive-search adaptive-web user-profile Recent advances in graph-based search techniques derived from Kleinberg's work [1] have been impressive. This paper further improves the graph-based search algo- rithm in two dimensions. Firstly, variants of Kleinberg's techniques do not take into account the semantics of the query string nor of the nodes being searched. As a result, polysemy of query words cannot be resolved. This paper presents an interactive query scheme utilizing the simple web ontology provided by the Open Directory...http://www.bibsonomy.org/bibtex/2fbfa7f20379bfbb48e11d8f66ce50e55/brusilovskybrusilovsky2009-07-01T11:12:30+02:00adaptive-search adaptive-web user-profile <span class="authorEditorList"><a href="/author/Sun">J. Sun</a>, <a href="/author/Zeng">H. Zeng</a>, <a href="/author/Liu">H. Liu</a>, <a href="/author/Lu">Y. Lu</a>, and <a href="/author/Chen">Z. Chen</a> </span>(<em>2005</em>)Wed Jul 01 11:12:30 CEST 2009Cubesvd: A novel approach to personalized web search2005adaptive-search adaptive-web user-profile As the competition of Web search market increases, there is a high demand for personalized Web search to conduct retrieval incorporating Web users' information needs. This paper focuses on utilizing clickthrough data to improve Web search. Since millions of searches are conducted everyday, a search engine accumulates a large volume of clickthrough data, which records who submits queries and which pages he/she clicks on. The clickthrough data is highly sparse and contains di\#erent types of...http://www.bibsonomy.org/bibtex/28445bcd8702b06128d4e04ea93430a59/brusilovskybrusilovsky2009-07-01T11:12:30+02:00adaptive-web ontology user-profile <span class="authorEditorList"><a href="/author/Chaffee">Jason Chaffee</a>, and <a href="/author/Gauch">Susan Gauch</a> </span><em>CIKM, </em><em>page 227--234. </em>(<em>2000</em>)Wed Jul 01 11:12:30 CEST 2009CIKM227--234Personal Ontologies for Web Navigation2000adaptive-web ontology user-profile The publicly indexable Web contains an estimated 800 million pages, however it is estimated that the largest search engine contains only 300 million of these pages. As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Often users must browse through a large hierarchy of categories to find the information for which they are looking. To provide the user with the...http://www.bibsonomy.org/bibtex/2d4785a365a67a0944b65354c76d13c87/brusilovskybrusilovsky2009-07-01T11:12:30+02:00adaptive-search en ontology user-profile www-search <span class="authorEditorList"><a href="/author/Chirita">Paul A. Chirita</a>, <a href="/author/Nejdl">Wolfgang Nejdl</a>, <a href="/author/Paiu">Raluca Paiu</a>, and <a href="/author/Kohlsch\&amp;\#252;tter">Christian Kohlsch\&amp;\#252;tter</a> </span><em>SIGIR &#039;05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, </em><em>page 178--185. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2005</em>)Wed Jul 01 11:12:30 CEST 2009New York, NY, USASIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval178--185Using ODP metadata to personalize search2005adaptive-search en ontology user-profile www-search The Open Directory Project is clearly one of the largest collaborative efforts to manually annotate web pages. This effort involves over 65,000 editors and resulted in metadata specifying topic and importance for more than 4 million web pages. Still, given that this number is just about 0.05 percent of the Web pages indexed by Google, is this effort enough to make a difference? In this paper we discuss how these metadata can be exploited to achieve high quality personalized web search. First, we address this by introducing an additional criterion for web page ranking, namely the distance between a user profile defined using ODP topics and the sets of ODP topics covered by each URL returned in regular web search. We empirically show that this enhancement yields better results than current web search using Google. Then, in the second part of the paper, we investigate the boundaries of biasing PageRank on subtopics of the ODP in order to automatically extend these metadata to the whole web.http://www.bibsonomy.org/bibtex/2c1e0e092c775e89fef5e32786c3c2bb0/brusilovskybrusilovsky2009-07-01T11:12:30+02:00news user-profile <span class="authorEditorList"><a href="/author/Magnini">Bernardo Magnini</a>, and <a href="/author/Strapparava">Carlo Strapparava</a> </span><em>User Modeling and User-Adapted Interaction</em> <em>14(2-3):239--257</em> (<em>June 2004</em>)Wed Jul 01 11:12:30 CEST 2009User Modeling and User-Adapted InteractionJune2-3239--257User Modelling for News Web Sites with Word Sense Based Techniques142004news user-profile SiteIF is a personal agent for a bilingual news web site that learns user's interests from the requested pages. In this paper we propose to use a word sense based document representation as a starting point to build a model of the user's interests. Documents passed over are processed and relevant senses (disambiguated over WordNet) are extracted and then combined to form a semantic network. A filtering procedure dynamically predicts new documents on the basis of the semantic network. There are two main advantages of a sense-based approach: first, the model predictions, being based on senses rather than words, are more accurate; second, the model is language independent, allowing navigation in multilingual sites. We report the results of a comparative experiment that has been carried out to give a quantitative estimation of these improvements.http://www.bibsonomy.org/bibtex/251292c78d76f5461371784280b7ff1c5/brusilovskybrusilovsky2009-07-01T11:12:30+02:00implicit-feedback information-retrieval user-profile <span class="authorEditorList"><a href="/author/Kelly">Diane Kelly</a>, and <a href="/author/Teevan">Jaime Teevan</a> </span><em>SIGIR Forum</em> <em>37(2):18--28</em> (<em>2003</em>)Wed Jul 01 11:12:30 CEST 2009New York, NY, USASIGIR Forum218--28Implicit feedback for inferring user preference: a bibliography372003implicit-feedback information-retrieval user-profile http://www.bibsonomy.org/bibtex/2724c9dda5bb43c41e0a129ea8b7190c8/brusilovskybrusilovsky2009-07-01T11:12:30+02:00personalization usage-mining user-profile <span class="authorEditorList"><a href="/author/Flesca">Sergio Flesca</a>, <a href="/author/Greco">Sergio Greco</a>, <a href="/author/Tagarelli">Andrea Tagarelli</a>, and <a href="/author/Zumpano">Ester Zumpano</a> </span><em>World Wide Web</em> <em>8(3):317--345</em> (<em>September 2005</em>)Wed Jul 01 11:12:30 CEST 2009World Wide WebSeptember3317--345Mining User Preferences, Page Content and Usage to Personalize Website Navigation82005personalization usage-mining user-profile http://www.bibsonomy.org/bibtex/2d5197251b456325659aceb68eb2032bc/brusilovskybrusilovsky2009-07-01T11:12:30+02:00adaptive-search adaptive-web user-profile <span class="authorEditorList"><a href="/author/Liu">Fang Liu</a>, <a href="/author/Yu">Clement Yu</a>, and <a href="/author/Meng">Weiyi Meng</a> </span><em>CIKM &#039;02: Proceedings of the eleventh international conference on Information and knowledge management, </em><em>page 558--565. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2002</em>)Wed Jul 01 11:12:30 CEST 2009New York, NY, USACIKM '02: Proceedings of the eleventh international conference on Information and knowledge management558--565Personalized web search by mapping user queries to categories2002adaptive-search adaptive-web user-profile http://www.bibsonomy.org/bibtex/2dabd533974af3a65a5341dbfce45dbf3/brusilovskybrusilovsky2009-07-01T11:12:30+02:00classification news recommender user-profile <span class="authorEditorList"><a href="/author/Billsus">D. Billsus</a>, and <a href="/author/Pazzani">M. Pazzani</a> </span>(<em>1999</em>)Wed Jul 01 11:12:30 CEST 2009A Hybrid User Model for News Story Classification1999classification news recommender user-profile . We present an intelligent agent designed to compile a daily news program for individual users. Based on feedback from the user, the system automatically adapts to the user's preferences and interests. In this paper we focus on the system's user modeling component. First, we motivate the use of a multi-strategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the utility of...http://www.bibsonomy.org/bibtex/2d5bcedb7b1c75f627b02d97266071f18/brusilovskybrusilovsky2009-07-01T11:12:30+02:00news personalization recommender user-profile <span class="authorEditorList"><a href="/author/Billsus">D. Billsus</a>, and <a href="/author/Pazzani">M. Pazzani</a> </span>(<em>2000</em>)Wed Jul 01 11:12:30 CEST 2009User Modeling for Adaptive News Access2000news personalization recommender user-profile We present a framework for adaptive news access, based on machine learning techniques specically designed for this task. First, we focus on the system's general functionality and system architecture.We then describe the interface and design of two deployed news agents that are part of the described architecture. While the rst agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) ...http://www.bibsonomy.org/bibtex/2c92ec3c2508ba152c7642bc856293d72/brusilovskybrusilovsky2009-07-01T11:12:30+02:00museum ontology user-model user-profile <span class="authorEditorList"><a href="/author/Hatala">Marek Hatala</a>, and <a href="/author/Wakkary">Ron Wakkary</a> </span><em>User Modeling and User-Adapted Interaction</em> <em>15(3-4):339--380</em> (<em>August 2005</em>)Wed Jul 01 11:12:30 CEST 2009User Modeling and User-Adapted InteractionAugust3-4339--380Ontology-Based User Modeling in an Augmented Audio Reality System for Museums152005museum ontology user-model user-profile http://www.bibsonomy.org/bibtex/2a2f608b6612797c29020cf75e9daa75f/brusilovskybrusilovsky2009-07-01T11:12:30+02:00recommender user-profile <span class="authorEditorList"><a href="/author/Bonhard">Philip Bonhard</a>, <a href="/author/Harries">Clare Harries</a>, <a href="/author/Mccarthy">John Mccarthy</a>, and <a href="/author/Sasse">Angela M. Sasse</a> </span><em>CHI &#039;06: Proceedings of the SIGCHI conference on Human Factors in computing systems, </em><em>page 1057--1066. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2006</em>)Wed Jul 01 11:12:30 CEST 2009New York, NY, USACHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems1057--1066Accounting for taste: using profile similarity to improve recommender systems2006recommender user-profile http://www.bibsonomy.org/bibtex/2b10a4fa9183c6cb5ab7f793a218a42f5/brusilovskybrusilovsky2009-07-01T11:12:30+02:00digital-library en user-profile <span class="authorEditorList"><a href="/author/Frias-Martinez">E. Frias-Martinez</a>, <a href="/author/Magoulas">G. Magoulas</a>, <a href="/author/Chen">S. Chen</a>, and <a href="/author/Macredie">R. Macredie</a> </span><em>International Journal of Information Management</em> <em>26(3):234--248</em> (<em>June 2006</em>)Wed Jul 01 11:12:30 CEST 2009International Journal of Information ManagementJune3234--248Automated user modeling for personalized digital libraries262006digital-library en user-profile Digital libraries (DLs) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from DLs. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in DLs has been user driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct DLs that satisfy a user's necessity for information: Adaptive DLs, libraries that automatically learn user preferences and goals and personalize their interaction using this information.http://www.bibsonomy.org/bibtex/2a93e13bddf45aec29556f69c6ea06c9c/brusilovskybrusilovsky2009-07-01T11:12:30+02:00implicit-feedback user-profile <span class="authorEditorList"><a href="/author/Dumais">Susan Dumais</a>, <a href="/author/Joachims">Thorsten Joachims</a>, <a href="/author/Bharat">Krishna Bharat</a>, and <a href="/author/Weigend">Andreas Weigend</a> </span><em>SIGIR Forum</em> <em>37(2):50--54</em> (<em>2003</em>)Wed Jul 01 11:12:30 CEST 2009New York, NY, USASIGIR Forum250--54SIGIR 2003 workshop report: implicit measures of user interests and preferences372003implicit-feedback user-profile http://www.bibsonomy.org/bibtex/2b6117fc6c20bf9d528a92cb4cfd4bdb4/brusilovskybrusilovsky2009-07-01T11:12:30+02:00adaptive-web-site context user-profile <span class="authorEditorList"><a href="/author/Rosaci">Domenico Rosaci</a>, and <a href="/author/Sarne">Giuseppe Sarne</a> </span><em>User Modeling and User-Adapted Interaction</em> <em>16(5):435--462</em> (<em>December 2006</em>)Wed Jul 01 11:12:30 CEST 2009User Modeling and User-Adapted InteractionDecember5435--462MASHA: A multi-agent system handling user and device adaptivity of Web sites162006adaptive-web-site context user-profile http://www.bibsonomy.org/bibtex/2bf8f043142faf6f82b57b18b1f9c37cb/brusilovskybrusilovsky2009-07-01T11:12:30+02:00adaptive-search en user-profile <span class="authorEditorList"><a href="/author/Liu">Fang Liu</a>, <a href="/author/Yu">Clement Yu</a>, and <a href="/author/Meng">Weiyi Meng</a> </span><em>IEEE Transactions on Knowledge and Data Engineering</em> <em>16(1):28--40</em> (<em>January 2004</em>)Wed Jul 01 11:12:30 CEST 2009Piscataway, NJ, USAIEEE Transactions on Knowledge and Data EngineeringJanuary128--40Personalized Web Search For Improving Retrieval Effectiveness162004adaptive-search en user-profile Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.http://www.bibsonomy.org/bibtex/2710682ddd9ed2c9bd517b8e6844ba0f2/brusilovskybrusilovsky2009-07-01T11:12:30+02:00adaptive-filtering news open-user-model recommender user-profile <span class="authorEditorList"><a href="/author/Ahn">Jae-Wook Ahn</a>, <a href="/author/Brusilovsky">Peter Brusilovsky</a>, <a href="/author/Grady">Jonathan Grady</a>, <a href="/author/He">Daqing He</a>, and <a href="/author/Syn">Sue Y. Syn</a> </span><em>WWW &#039;07: Proceedings of the 16th international conference on World Wide Web, </em><em>page 11--20. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2007</em>)Wed Jul 01 11:12:30 CEST 2009New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web11--20Open user profiles for adaptive news systems: help or harm?2007adaptive-filtering news open-user-model recommender user-profile http://www.bibsonomy.org/bibtex/25892feb3f219c713dc5293f2bdf10fa9/brusilovskybrusilovsky2009-07-01T11:12:30+02:00adaptive-search adaptive-web personalization user-profile <span class="authorEditorList"><a href="/author/Ma">Zhongming Ma</a>, <a href="/author/Pant">Gautam Pant</a>, and <a href="/author/Olivia"> Olivia</a> </span><em>ACM Trans. Inf. Syst.</em> (<em>February 2007</em>)Wed Jul 01 11:12:30 CEST 2009New York, NY, USAACM Trans. Inf. Syst.February1Interest-based personalized search252007adaptive-search adaptive-web personalization user-profile Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps a set of known user interests onto a group of categories in the Open Directory Project (ODP) and takes advantage of manually edited data available in ODP for training text classifiers that correspond to, and therefore categorize and personalize search results according to user interests. In two sets of controlled experiments, we compare our personalized categorization system (PCAT) with a list interface system (LIST) that mimics a typical search engine and with a nonpersonalized categorization system (CAT). In both experiments, we analyze system performances on the basis of the type of task and query length. We find that PCAT is preferable to LIST for information gathering types of tasks and for searches with short queries, and PCAT outperforms CAT in both information gathering and finding types of tasks, and for searches associated with free-form queries. From the subjects' answers to a questionnaire, we find that PCAT is perceived as a system that can find relevant Web pages quicker and easier than LIST and CAT.http://www.bibsonomy.org/bibtex/28e8c1537f21b3027f9567f9ab89ce2ce/brusilovskybrusilovsky2009-07-01T11:12:30+02:00news personalization user-profile <span class="authorEditorList"><a href="/author/Widyantoro">Dwi H. Widyantoro</a>, <a href="/author/Ioerger">Thomas R. Ioerger</a>, and <a href="/author/Yen">John Yen</a> </span><em>CIKM &#039;99: Proceedings of the eighth international conference on Information and knowledge management, </em><em>page 405--412. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>November 1999</em>)Wed Jul 01 11:12:30 CEST 2009New York, NY, USACIKM '99: Proceedings of the eighth international conference on Information and knowledge managementNovember405--412An adaptive algorithm for learning changes in user interests1999news personalization user-profile