Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Fab: content-based, collaborative recommendation
Communications of the ACM
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering informative content blocks from Web documents
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic Fragment Detection in Dynamic Web Pages and Its Impact on Caching
IEEE Transactions on Knowledge and Data Engineering
Automatic Identification of Informative Sections of Web Pages
IEEE Transactions on Knowledge and Data Engineering
Informed Recommender: Basing Recommendations on Consumer Product Reviews
IEEE Intelligent Systems
MarCol: A Market-Based Recommender System
IEEE Intelligent Systems
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
TV3P: an adaptive assistant for personalized TV
IEEE Transactions on Consumer Electronics
Overview of the MPEG-7 standard
IEEE Transactions on Circuits and Systems for Video Technology
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The amount of information on the Web is rapidly increasing. Recommender systems can help users selectively filter this information based on their preferences. One way to obtain user preferences is to analyze characteristics of content that is accessed by the user. Unfortunately, web pages may contain elements irrelevant to user interests (e.g., navigation bar, advertisements, and links.). Hence, existing analysis approaches using the TF-IDF method may not be suitable. This paper proposes a novel user preference analysis system that eliminates elements that repeatedly appear in web pages. It extracts user interest keywords in the identified primary content. Also, the system has features that collect the anchor tag, and track the user's search route, in order to identify keywords that are of core interest to the user. This paper compares the proposed system with pure TF-IDF analysis method. The analysis confirms its effectiveness in terms of the accuracy of the analyzed user profiles.