Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Learning to recommend from positive evidence
Proceedings of the 5th international conference on Intelligent user interfaces
Learning user interest dynamics with a three-descriptor representation
Journal of the American Society for Information Science and Technology
PVA: A Self-Adaptive Personal View Agent
Journal of Intelligent Information Systems
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Dynamic pattern mining: an incremental data clustering approach
Journal on Data Semantics II
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With the spread of the digital library and the web, users can obtain a wide variety of information, and also can access novel content. In this environment, finding useful information from a huge amount of available content becomes a time consuming process. In this paper, we focus on user modeling for personalization to recommend content relevant to user interests. We exploit the data mining techniques for identifying useful and meaningful patterns of users. Each user model, collectively called PTP (Personalized Term Pattern), is represented as both interest patterns and disinterest patterns. We present empirical experiments using NSF research award datasets to demonstrate our approach and evaluate performance compared with existing methods.