Towards a graph-based user profile modeling for a session-based personalized search

Most Web search engines use the content of the Web documents and their link structures to assess the relevance of the document to the user’s query. With the growth of the information available on the web, it becomes difficult for such Web search engines to satisfy the user information need expressed by few keywords. First, personalized information retrieval is a promising way to resolve this problem by modeling the user profile by his general interests and then integrating it in a personalized document ranking model. In this paper, we present a personalized search approach that involves a graph-based representation of the user profile. The user profile refers to the user interest in a specific search session defined as a sequence of related queries. It is built by means of score propagation that allows activating a set of semantically related concepts of reference ontology, namely the ODP. The user profile is maintained across related search activities using a graph-based merging strategy. For the purpose of detecting related search activities, we define a session boundary recognition mechanism based on the Kendall rank correlation measure that tracks changes in the dominant concepts held by the user profile relatively to a new submitted query. Personalization is performed by re-ranking the search results of related queries using the user profile. Our experimental evaluation is carried out using the HARD 2003 TREC collection and showed that our session boundary recognition mechanism based on the Kendall measure provides a significant precision comparatively to other non-ranking based measures like the cosine and the WebJaccard similarity measures. Moreover, results proved that the graph-based search personalization is effective for improving the search accuracy.

[1]  Djoerd Hiemstra,et al.  Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002 , 2003, SIGF.

[2]  Dan Klein,et al.  Evaluating strategies for similarity search on the web , 2002, WWW '02.

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

[4]  Filippo Menczer,et al.  Algorithmic detection of semantic similarity , 2005, WWW '05.

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

[6]  Mohand Boughanem,et al.  Learning user interests for a session-based personalized search , 2008, IIiX.

[7]  Yong Yu,et al.  Using Probabilistic Latent Semantic Analysis for Personalized Web Search , 2005, APWeb.

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

[9]  Xiangji Huang,et al.  Applying language modeling to session identification from database trace logs , 2006, Knowledge and Information Systems.

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

[11]  Korris Fu-Lai Chung,et al.  Knowledge and Information Systems , 2017 .

[12]  Georgia Koutrika,et al.  A Unified User Profile Framework for Query Disambiguation and Personalization , 2005 .

[13]  Mohand Boughanem,et al.  A session based personalized search using an ontological user profile , 2009, SAC '09.

[14]  Bamshad Mobasher,et al.  Data Mining for Web Personalization , 2007, The Adaptive Web.

[15]  Chen Ding,et al.  Personalized Web search with self-organizing map , 2005, 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service.

[16]  Wolfgang Nejdl,et al.  Using ODP metadata to personalize search , 2005, SIGIR '05.

[17]  Henry Lieberman,et al.  Letizia: An Agent That Assists Web Browsing , 1995, IJCAI.

[18]  John R. Paul,et al.  A Multiple Model Approach to Personalised Information Access , 2003 .

[19]  Filippo Menczer,et al.  Topical web crawlers: Evaluating adaptive algorithms , 2004, TOIT.

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

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

[22]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[23]  Ah-Hwee Tan,et al.  Towards personalised web intelligence , 2004, Knowledge and Information Systems.

[24]  Amanda Spink,et al.  U.S. versus European web searching trends , 2002, SIGF.

[25]  Robert Ivor John,et al.  Fuzzy User Modeling for Information Retrieval on the World Wide Web , 2001, Knowledge and Information Systems.

[26]  W. Bruce Croft,et al.  Measuring ranked list robustness for query performance prediction , 2007, Knowledge and Information Systems.

[27]  Alessandro Micarelli,et al.  Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System , 2004, User Modeling and User-Adapted Interaction.

[28]  Geoffrey I. Webb,et al.  # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Machine Learning for User Modeling , 1999 .

[29]  Osmar R. Zaïane,et al.  Clustering Web sessions by sequence alignment , 2002, Proceedings. 13th International Workshop on Database and Expert Systems Applications.

[30]  Wei-Ying Ma,et al.  Web-page classification through summarization , 2004, SIGIR '04.

[31]  Amanda Spink,et al.  How to Define Searching Sessions on Web Search Engines , 2006, WEBKDD.

[32]  ChengXiang Zhai,et al.  A session-based search engine , 2004, SIGIR '04.

[33]  ChengXiang Zhai,et al.  Mining long-term search history to improve search accuracy , 2006, KDD '06.

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

[35]  Filippo Menczer,et al.  A General Evaluation Framework for Topical Crawlers , 2005, Information Retrieval.

[36]  Mohand Boughanem,et al.  Connexionist and genetic approaches to achieve IR , 2000 .

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

[38]  Dale Schuurmans,et al.  Dynamic Web log session identification with statistical language models , 2004, J. Assoc. Inf. Sci. Technol..

[39]  Feng Qiu,et al.  Automatic identification of user interest for personalized search , 2006, WWW '06.

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

[41]  Daqing He,et al.  Detecting session boundaries from Web user logs , 2000 .

[42]  Mohand Boughanem,et al.  A Study on Using Genetic Niching for Query Optimisation in Document Retrieval , 2002, ECIR.

[43]  Andrew Foss,et al.  A non-parametric approach to web log analysis , 2001 .

[44]  Robin Burke,et al.  USING CONCEPT HIERARCHIES TO ENHANCE USER QUERIES IN WEB-BASED INFORMATION RETRIEVAL , 2003 .

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

[46]  Philip K. Chan,et al.  Learning implicit user interest hierarchy for context in personalization , 2008, IUI '03.

[47]  Fabio Crestani,et al.  Soft computing in information retrieval: techniques and applications , 2000 .

[48]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[49]  Iain M. Begg,et al.  A prototype intelligent user interface for real-time supervisory control systems , 1993, IUI '93.

[50]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

[51]  Mohand Boughanem,et al.  Personalized document ranking: Exploiting evidence from multiple user interests for profiling and retrieval , 2008, J. Digit. Inf. Manag..

[52]  Mohand Boughanem,et al.  Connectionist and Genetic Approaches for Information Retrieval , 2000 .

[53]  Yi-Shin Chen,et al.  Web Information Personalization: Challenges and Approaches , 2003, DNIS.

[54]  Philip S. Yu,et al.  Efficient Data Mining for Path Traversal Patterns , 1998, IEEE Trans. Knowl. Data Eng..

[55]  Diane Kelly,et al.  Implicit feedback for inferring user preference , 2003 .

[56]  Henry Lieberman,et al.  Autonomous interface agents , 1997, CHI.

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