User profiles and fuzzy logic for web retrieval issues

Abstract We present a study of the role of user profiles using fuzzy logic in web retrieval processes. Flexibility for user interaction and for adaptation in profile construction becomes an important issue. We focus our study on user profiles, including creation, modification, storage, clustering and interpretation. We also consider the role of fuzzy logic and other soft computing techniques to improve user profiles. Extended profiles contain additional information related to the user that can be used to personalize and customize the retrieval process as well as the web site. Web mining processes can be carried out by means of fuzzy clustering of these extended profiles and fuzzy rule construction. Fuzzy inference can be used in order to modify queries and extract knowledge from profiles with marketing purposes within a web framework. An architecture of a portal that could support web mining technology is also presented.

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

[2]  Ralf Walther,et al.  The Data Webhouse Toolkit , 2001, Künstliche Intell..

[3]  M. Vila,et al.  Intelligent filtering with genetic algorithms and fuzzy logic , 2002 .

[4]  Beerud Dilip Sheth,et al.  A learning approach to personalized information filtering , 1994 .

[5]  Philippe Smets,et al.  Imperfect Information: Imprecision and Uncertainty , 1996, Uncertainty Management in Information Systems.

[6]  Donald H. Kraft,et al.  A model for a weighted retrieval system , 1981, J. Am. Soc. Inf. Sci..

[7]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[8]  F. Berzal,et al.  Computing with words in information retrieval , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[9]  Donald H. Kraft,et al.  Vocabulary mining for information retrieval: rough sets and fuzzy sets , 2001, Inf. Process. Manag..

[10]  Bamshad Mobasher,et al.  Improving the Effectiveness of Collaborative Filtering on Anonymous Web Usage Data , 2001 .

[11]  Rami Zwick,et al.  Measures of similarity among fuzzy concepts: A comparative analysis , 1987, Int. J. Approx. Reason..

[12]  Antonio F. Gómez-Skarmeta,et al.  On the use of hierarchical clustering in fuzzy modeling , 1996, Int. J. Approx. Reason..

[13]  Ah-Hwee Tan,et al.  Learning user profiles for personalized information dissemination , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[14]  Robert R. Korfhage,et al.  Query Optimization in Information Retrieval Using Genetic Algorithms , 1993, ICGA.

[15]  M.J. Martin-Bautista,et al.  Building adaptive user profiles by a genetic fuzzy classifier with feature selection , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[16]  Henrik Legind Larsen,et al.  A fuzzy genetic algorithm approach to an adaptive information retrieval agent , 1999 .

[17]  Antonio F. Gómez-Skarmeta,et al.  About the use of fuzzy clustering techniques for fuzzy model identification , 1999, Fuzzy Sets Syst..

[18]  Hsinchun Chen Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms , 1995 .

[19]  Jesus Mena Data Mining Your Website , 1999 .

[20]  M. A. Vila,et al.  Pattern recognition with evidential knowledge , 1999 .

[21]  Herbert Stoyan,et al.  The Knowledge Discovery Assistant: Making Data Mining Available for Business Users , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[22]  Anupam Joshi,et al.  Extracting Web User Profiles Using Relational Competitive Fuzzy Clustering , 2000, Int. J. Artif. Intell. Tools.