Intelligent information-sharing systems
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
DEBORA: Developing an Interface to Support Collaboration in a Digital Library
ECDL '00 Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries
An XML Log Standard and Tool for Digital Library Logging Analysis
ECDL '02 Proceedings of the 6th European Conference on Research and Advanced Technology for Digital Libraries
Guiding knowledge discovery through interactive data mining
Managing data mining technologies in organizations
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Enhancing usability in CITIDEL: multimodal, multilingual, and interactive visualization interfaces
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
Communications of the ACM - The Blogosphere
Interest-based user grouping model for collaborative filtering in digital libraries
ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
ICADL'06 Proceedings of the 9th international conference on Asian Digital Libraries: achievements, Challenges and Opportunities
Data Mining and Knowledge Discovery
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Most user focused data mining techniques involve purchase pattern analysis, targeted at strictly-formatted database-like transaction records. Most personalization systems employ explicitly provided user preferences rather than implicit rating data obtained automatically by collecting users' interactions. In this paper, we show that in complex information systems such as digital libraries, implicit rating data can help to characterize users' research and learning interests, and can be used to cluster users into meaningful groups. Thus, in our personalized recommender system based on collaborative filtering, we employ a user tracking system and a user modeling technique to capture and store users' implicit ratings. Also, we describe the effects (on community finding) of using four different types of implicit rating data.