User Preference Mining through Hybrid Collaborative Filtering and Content-Based Filtering in Recommendation System

Kyung-Yong JUNG  Jung-Hyun LEE  

IEICE TRANSACTIONS on Information and Systems   Vol.E87-D   No.12   pp.2781-2790
Publication Date: 2004/12/01
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence and Cognitive Science
data mining,  collaborative filtering,  content-based filtering,  machine learning,  retrieval information,  hybrid filtering,  

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The growth of the Internet has resulted in an increasing need for personalized information systems. The paper describes an autonomous agent, the Web Robot Agent or WebBot, which integrates with the web and acts as a personal recommendation system that cooperates with the user in order to identify interesting pages. The Apriori algorithm extracts the characteristics of the web pages in the form of association words that are semantically related and mines a bag of association words. Using hybrid components from collaborative filtering and content-based filtering, this hybrid recommendation system can overcome the shortcomings associated with traditional recommendation systems. In this paper, we present an improved recommendation system, which uses the user preference mining through hybrid 2-way filtering. The proposed method was tested on a database, and its effectiveness compared with existent methods was proven in on-line experiments.