Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Extraction and analysis of knowledge worker activities on intranet
PAKM'06 Proceedings of the 6th international conference on Practical Aspects of Knowledge Management
Optimizing multiple objectives in collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
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Our approach aims to provide a mechanism for recommending long tail items to knowledge workers. The approach employs collaborative filtering using browsing features of identified key population of the diffusion of information. We conducted analytic experiment for a novel recommendation algorithm based on the browsing features of identified selected users and discovered that the first 10 users accessing a particular page play the key role in information spread. The evaluation indicated that our approach is effective for long tail recommendation.