Adaptive information retrieval system based on fuzzy profiling

The importance of finding relevant information for business and decision making is imperative for both individuals as well as enterprises. In this paper, we present an approach for the development of a fuzzy information retrieval (IR) system. The approach provides a new mechanism for constructing and integrating three relevancy profiles comprising of: a task profile, user profile and document profile, into a unified index, through the use of relevance feedback and fuzzy rule based summarisation. Experiments were performed from which relevance feedback and user queries were captured from 35 users on 20 predefined simulated enterprise search tasks. The captured data set was used to develop the three types of profiles and train the fuzzy system. The system shows 86% performance accuracy in correctly classifying document relevance. The overall performance of the system was evaluated based on standard precision and recall which shows significant improvements in retrieving relevant documents based on user queries.

[1]  Ryen W. White,et al.  Large-scale analysis of individual and task differences in search result page examination strategies , 2012, WSDM '12.

[2]  Jian Wang,et al.  Utilizing re-finding for personalized information retrieval , 2010, CIKM '10.

[3]  Gabriella Kazai,et al.  Proceedings of the fifth ACM workshop on Research advances in large digital book repositories and complementary media , 2010, CIKM 2010.

[4]  Patrice Perny,et al.  Preference-based Search and Machine Learning for Collaborative Filtering: the "Film-Conseil" Movie Recommender System , 2001 .

[5]  Jaime Teevan,et al.  Large scale query log analysis of re-finding , 2010, WSDM '10.

[6]  Anne E. James,et al.  Towards the Development of an Integrated Framework for Enhancing Enterprise Search Using Latent Semantic Indexing , 2011, ICCS.

[7]  Yiqun Liu,et al.  How do users describe their information need: Query recommendation based on snippet click model , 2011, Expert Syst. Appl..

[8]  Mark Claypool,et al.  Inferring User Interest , 2001, IEEE Internet Comput..

[9]  Analía Amandi,et al.  Intelligent User Profiling , 2009, Artificial Intelligence: An International Perspective.

[10]  Chris Cornelis,et al.  One-and-only item recommendation with fuzzy logic techniques , 2007, Inf. Sci..

[11]  Luis M. de Campos,et al.  A collaborative recommender system based on probabilistic inference from fuzzy observations , 2008, Fuzzy Sets Syst..

[12]  Eugene Agichtein,et al.  Beyond dwell time: estimating document relevance from cursor movements and other post-click searcher behavior , 2012, WWW.

[13]  Alan Eckhardt.  Similarity of users' (content-based) preference models for Collaborative filtering in few ratings scenario , 2012, Expert Syst. Appl..

[14]  Bruno Martins,et al.  Stylometric relevance-feedback towards a hybrid book recommendation algorithm , 2012, BooksOnline '12.

[15]  José M. Molina López,et al.  Agent-based collaborative filtering based on fuzzy recommendations , 2004, Int. J. Web Eng. Technol..

[16]  Rahat Iqbal,et al.  An intelligent framework for monitoring student performance using fuzzy rule-based Linguistic Summarisation , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[17]  Javier Carbó,et al.  Agent-based collaborative filtering based on fuzzy recommendations , 2004 .

[18]  M. F. Porter,et al.  An algorithm for suffix stripping , 1997 .

[19]  Douglas W. Oard,et al.  Using Implicit Feedback for User Modeling in Internet and Intranet Searching ϕ , 2000 .

[20]  Vimala Balakrishnan,et al.  Implicit user behaviours to improve post-retrieval document relevancy , 2014, Comput. Hum. Behav..

[21]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[22]  Hisao Ishibuchi,et al.  Rule weight specification in fuzzy rule-based classification systems , 2005, IEEE Transactions on Fuzzy Systems.

[23]  Ricardo Baeza-Yates,et al.  Query-sets: using implicit feedback and query patterns to organize web documents , 2008, WWW.

[24]  Nasser Yazdani,et al.  A3CRank: An adaptive ranking method based on connectivity, content and click-through data , 2010, Inf. Process. Manag..

[25]  Ryen W. White,et al.  Text selections as implicit relevance feedback , 2012, SIGIR '12.

[26]  Yoichi Shinoda,et al.  Information filtering based on user behavior analysis and best match text retrieval , 1994, SIGIR '94.

[27]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[28]  Hani Hagras,et al.  A fuzzy based agent for group decision support of applicants ranking within recruitment systems , 2009, 2009 IEEE Symposium on Intelligent Agents.

[29]  Rahat Iqbal,et al.  An intelligent framework for activity led learning in network planning and management , 2014, Int. J. Commun. Networks Distributed Syst..

[30]  Jerry M. Mendel,et al.  Linguistic summarization using IF-THEN rules , 2010, International Conference on Fuzzy Systems.

[31]  Martin Szomszor,et al.  Comparison of implicit and explicit feedback from an online music recommendation service , 2010, HetRec '10.

[32]  Ronald R. Yager,et al.  Fuzzy logic methods in recommender systems , 2003, Fuzzy Sets Syst..

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

[34]  Jonathan L. Herlocker,et al.  Click data as implicit relevance feedback in web search , 2007, Inf. Process. Manag..

[35]  Byeong Man Kim,et al.  Constructing User Profiles for Collaborative Recommender System , 2004, APWeb.

[36]  Rahat Iqbal,et al.  Task-specific information retrieval systems for software engineers , 2012, J. Comput. Syst. Sci..