Dynamic modeling and learning user profile in personalized news agent

Finding relevant information eeectively on the Internet is a challenging task. Although the information is widely available, exploring Web sites and nding information relevant to a user's interest can be a time-consuming and tedious task. As a result, many software agents have been employed to perform autonomous information gathering and ltering on behalf of the user. One of the critical issues in such an agent is the capability of the agent to model its users and adapt itself over time to changing user interests. In this thesis, a novel scheme is proposed to learn user proole. The proposed scheme is designed to handle multiple domains of long-term and short-term users' interests simultaneously, which are learned through positive and negative user feedback. A 3-descriptor interest category representation approach is developed to achieve this objective. Using such a representation, the learning algorithm is derived by imitating human personal assistants doing the same task. Based on experimental evaluation, the scheme performs very well and adapts quickly to signiicant changes in user interest. iv To my wife Lina Handayani and my daughter Risma Cahyani Widyantoro. v ACKNOWLEDGMENTS

[1]  J. Kenrick,et al.  Hybrid HillClimbing and Knowledge-Based Techniques for Intelligent News Filtering , 1996 .

[2]  Gerald Kowalski,et al.  Information Retrieval Systems: Theory and Implementation , 1997 .

[3]  Yoav Shoham,et al.  Learning to surf: multiagent systems for adaptive web page recommendation , 1998 .

[4]  Filippo Menczer,et al.  An Endogenous Fitness Paradigm for Adaptive Information Agents , 1994 .

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

[6]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[7]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[8]  Katia P. Sycara,et al.  WebMate: a personal agent for browsing and searching , 1998, AGENTS '98.

[9]  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).

[10]  George Kingsley Zipf,et al.  Human behavior and the principle of least effort , 1949 .

[11]  Giorgos Zacharia,et al.  Evolving a multi-agent information filtering solution in Amalthaea , 1997, AGENTS '97.

[12]  Thorsten Joachims,et al.  A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization , 1997, ICML.

[13]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[14]  S. Floyd,et al.  Adaptive Web , 1997 .

[15]  Yiyu Yao,et al.  Measuring Retrieval Effectiveness Based on User Preference of Documents , 1995, J. Am. Soc. Inf. Sci..

[16]  Hans Peter Luhn,et al.  The Automatic Creation of Literature Abstracts , 1958, IBM J. Res. Dev..

[17]  Andreas S. Weigend,et al.  A neural network approach to topic spotting , 1995 .

[18]  Marko Balabanovic,et al.  An adaptive Web page recommendation service , 1997, AGENTS '97.