Issues in Personalizing Information Retrieval

This paper shortly discusses the main issues related to the problem of personalizing search. To overcome the “one size fits all” behavior of most search engines and Information Retrieval Systems, in recent years a great deal of research has addressed the problem of defining techniques aimed at tailoring the search outcome to the user context. This paper outlines the main issues related to the two basic problems beyond these approaches: context representation and definition of processes which exploit the context knowledge to improve the quality of the search outcome. Moreover some other important and related issues are mentioned, such as privacy, and evaluation. Index Terms — Information Retrieval, Personalization, Context Modeling, User Modeling.

[1]  Donna K. Harman,et al.  Overview of the Fourth Text REtrieval Conference (TREC-4) , 1995, TREC.

[2]  Eugene Volokh,et al.  Personalization and privacy , 2000, CACM.

[3]  M. Claypool,et al.  Inferring User Interest , 2001, IEEE Internet Comput..

[4]  Hinrich Schütze,et al.  Personalized search , 2002, CACM.

[5]  Taher H. Haveliwala Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[6]  Jaime Teevan,et al.  Implicit feedback for inferring user preference: a bibliography , 2003, SIGF.

[7]  Jennifer Widom,et al.  Scaling personalized web search , 2003, WWW '03.

[8]  Alexander Pretschner,et al.  Ontology-based personalized search and browsing , 2003, Web Intell. Agent Syst..

[9]  James Allan,et al.  HARD Track Overview in TREC 2003: High Accuracy Retrieval from Documents , 2003, TREC.

[10]  Susan Gauch,et al.  Improving Ontology-Based User Profiles , 2004, RIAO.

[11]  Clement T. Yu,et al.  Personalized Web search for improving retrieval effectiveness , 2004, IEEE Transactions on Knowledge and Data Engineering.

[12]  Susan T. Dumais,et al.  Personalizing Search via Automated Analysis of Interests and Activities , 2005, SIGIR.

[13]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[14]  Susan Gauch,et al.  Personalizing Search Based on User Search Histories , 2004 .

[15]  Peter Ingwersen,et al.  The Turn - Integration of Information Seeking and Retrieval in Context , 2005, The Kluwer International Series on Information Retrieval.

[16]  Xuehua Shen,et al.  Context-sensitive information retrieval using implicit feedback , 2005, SIGIR '05.

[17]  Ryen W. White,et al.  Evaluating implicit feedback models using searcher simulations , 2005, TOIS.

[18]  ChengXiang Zhai,et al.  Implicit user modeling for personalized search , 2005, CIKM '05.

[19]  Susan T. Dumais,et al.  Learning user interaction models for predicting web search result preferences , 2006, SIGIR.

[20]  Alfred Kobsa,et al.  Privacy-enhanced personalization , 2006, FLAIRS.

[21]  Wolfgang Nejdl,et al.  Summarizing local context to personalize global web search , 2006, CIKM '06.

[22]  Alessandro Micarelli,et al.  User Profiles for Personalized Information Access , 2007, The Adaptive Web.

[23]  Ji-Rong Wen,et al.  WWW 2007 / Track: Search Session: Personalization A Largescale Evaluation and Analysis of Personalized Search Strategies ABSTRACT , 2022 .

[24]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[25]  Peter Brusilovsky,et al.  From User Query to User Model and Back: Adaptive Relevance-Based Visualization for Information Foraging , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[26]  Fabio Gasparetti,et al.  Personalized Search on the World Wide Web , 2007, The Adaptive Web.

[27]  Barry Smyth,et al.  A Community-Based Approach to Personalizing Web Search , 2007, Computer.

[28]  Bamshad Mobasher,et al.  Web search personalization with ontological user profiles , 2007, CIKM '07.

[29]  Olivia R. Liu Sheng,et al.  Interest-based personalized search , 2007, TOIS.

[30]  Paul-Alexandru Chirita,et al.  Personalized query expansion for the web , 2007, SIGIR.

[31]  Susan T. Dumais,et al.  To personalize or not to personalize: modeling queries with variation in user intent , 2008, SIGIR '08.

[32]  Nicholas J. Belkin,et al.  Some(what) grand challenges for information retrieval , 2008, SIGF.

[33]  Qi Li,et al.  Personalized web exploration with task models , 2008, WWW.

[34]  Mohand Boughanem,et al.  Towards a graph-based user profile modeling for a session-based personalized search , 2009, Knowledge and Information Systems.

[35]  Diane Kelly,et al.  Methods for Evaluating Interactive Information Retrieval Systems with Users , 2009, Found. Trends Inf. Retr..

[36]  Meredith Ringel Morris,et al.  Discovering and using groups to improve personalized search , 2009, WSDM '09.

[37]  Susan Gauch,et al.  Miology: A Web Application for Organizing Personal Domain Ontologies , 2009, 2009 International Conference on Information, Process, and Knowledge Management.

[38]  Mohand Boughanem,et al.  Evaluation of contextual information retrieval effectiveness: overview of issues and research , 2010, Knowledge and Information Systems.

[39]  Gabriella Pasi,et al.  Multidimensional Relevance: A New Aggregation Criterion , 2009, ECIR.

[40]  Gabriella Pasi,et al.  Ontology-Based Information Behaviour to Improve Web Search , 2010, Future Internet.

[41]  Mohand Boughanem,et al.  A Personalized Graph-Based Document Ranking Model Using a Semantic User Profile , 2010, UMAP.