Chapter 7 User Profiles Modeling in Information Retrieval Systems

The requirements imposed on information retrieval systems are increas- ing steadily. The vast number of documents in today's large databases and especially on the World Wide Web causes problems when searching for concrete information. It is difficult to find satisfactory information that accurately matches user infor- mation needs even if it is present in the database. One of the key elements when searching the web is proper formulation of user queries. Search effectiveness can be seen as the accuracy of matching user information needs against the retrieved information. As step towards better search systems represents personalized search based on user profiles. Personalized search applications can notably contribute to the improvement of web search effectiveness. This chapter presents design and experi- ments with an information retrieval system utilizing user profiles, fuzzy information retrieval and genetic algorithms for improvement of web search.

[1]  Hsinchun Chen,et al.  The use of dynamic contexts to improve casual internet searching , 2003, TOIS.

[2]  Ulrich Bodenhofer,et al.  Genetic Algorithms: Theory and Applications , 2002 .

[3]  Weiguo Fan,et al.  A generic ranking function discovery framework by genetic programming for information retrieval , 2004, Inf. Process. Manag..

[4]  M. Dianati,et al.  An Introduction to Genetic Algorithms and Evolution , 2002 .

[5]  Wei-Pang Yang,et al.  Learning to Rank for Information Retrieval Using Genetic Programming , 2007 .

[6]  Peter Ingwersen,et al.  Measures of relative relevance and ranked half-life: performance indicators for interactive IR , 1998, SIGIR '98.

[7]  Ed Greengrass,et al.  Information Retrieval: A Survey , 2000 .

[8]  Václav Snásel,et al.  Query optimization by Genetic Algorithms , 2005, DATESO.

[9]  Pertti Vakkari,et al.  Changes in relevance criteria and problem stages in task performance , 2000, J. Documentation.

[10]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[11]  V. Snasel,et al.  Implementing GP on Optimizing Boolean and Extended Boolean Queries in IRs With Respect to Users Profiles , 2006, 2006 International Conference on Computer Engineering and Systems.

[12]  Pavel Krömer,et al.  Evolving Web Search Expressions , 2007, Third International Conference on Natural Computation (ICNC 2007).

[13]  Václav Snásel,et al.  Evolutionary Learning of Boolean Queries by Genetic Programming , 2005, ADBIS Research Communications.

[14]  Donald H. Kraft,et al.  GENETIC ALGORITHMS FOR QUERY OPTIMIZATION IN INFORMATION RETRIEVAL: RELEVANCE FEEDBACK , 1997 .

[15]  Gareth Jones,et al.  Genetic and Evolutionary Algorithms , 2002 .

[16]  Oscar Cordón,et al.  Fuzzy logic and multiobjective evolutionary algorithms as soft computing tools for persistent query learning in text retrieval environments , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[17]  Makoto Haraguchi,et al.  An Appropriate Boolean Query Reformulation Interface for Information Retrieval Based on Adaptive Generalization , 2005, International Workshop on Challenges in Web Information Retrieval and Integration.

[18]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[19]  Wei-Ying Ma,et al.  Optimizing web search using web click-through data , 2004, CIKM '04.

[20]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[21]  Henry O. Nyongesa,et al.  User modelling using evolutionary interactive reinforcement learning , 2006, Information Retrieval.

[22]  A. Townsend.  Genetic Algorithms – a Tutorial , 2003 .

[23]  Fabio Crestani,et al.  Soft Information Retrieval : Applications of Fuzzy Set Theory and Neural Networks , 1999 .

[24]  Robert M. Losee.  When information retrieval measures agree about the relative quality of document rankings , 2000 .

[25]  Donald H. Kraft,et al.  Fuzzy Set Techniques in Information Retrieval , 1999 .

[26]  SaltonGerard,et al.  Term-weighting approaches in automatic text retrieval , 1988 .

[27]  Gloria Bordogna,et al.  Modeling Vagueness in Information Retrieval , 2000, ESSIR.

[28]  A. A. Aly.  APPLYING GENETIC ALGORITHM IN QUERY IMPROVEMENT PROBLEM , 2007 .

[29]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[30]  Václav Snásel,et al.  Implementing GP on Optimizing both Boolean and Extended Boolean Queries in IR and Fuzzy IR systems with Respect to the Users Profiles , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[31]  S. P. Harter.  Psychological relevance and information science , 1992 .

[32]  Nicholas J. Belkin,et al.  Information filtering and information retrieval: two sides of the same coin? , 1992, CACM.

[33]  Edward A. Fox,et al.  Ranking function optimization for effective Web search by genetic programming: an empirical study , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.