Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Siteseer: personalized navigation for the Web
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Let's browse: a collaborative Web browsing agent
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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With the fast development of World Wide Wed, web-based applications and services should allow users to get the right personalized information quickly and effectively. Collaborative Filtering plays a very important role in web service personalization and Recommender System. In this paper, Hesitation Degree was proposed to improve the accuracy of user based collaboration filtering and three kinds of Hesitation Degree were introduced into similarity computation. The results show that the prediction accuracy can be improved by 11 percents, and Mean Absolute Error can be reduced faster than classic method.