GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Efficient prediction of web accesses on a proxy server
Proceedings of the eleventh international conference on Information and knowledge management
Enabling Personalized Recommendation on the Web based on User Interests and Behaviors
Eleventh International Workshop on Research Issues in Data Engineering on Document Management for Data Intensive Business and Scientific Applications
A Community-Based Recommendation System to Reveal Unexpected Interests
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
IEEE Transactions on Knowledge and Data Engineering
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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The current collaborative recommendation approaches mainly measure users’ similarity by comparing user’s entire interests and don’t consider user’s interest quality, especially interest span. We propose a new approach to provide inter-website recommendation on proxy server based on partial similarity of interests, and construct corresponding user’s interest model to realize this method. According to psychological characteristic of interests, this approach divides user’s interest into several interest-points, which are correlated each other and farther divided into long-term interest and short-term interest. We mine the interest quality and correlation of interest-points from proxy log to construct user’s interest model. This method adopts different recommendation mechanism separately for long-term interests and short-term interests, which provides recommendation to target user’s long-term interests based on neighbors with partially similar interests and recommendation to user’s short-term interests based on experienced users. Experimental results indicate that this method can recommend interesting and unexpected inter-website pages to target users and improve the precision of personalized recommendation service on proxy server.