A collaborative recommender system based on asymmetric user similarity
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A recommender system based on multi-features
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
Constructing full matrix through Naïve Bayesian for collaborative filtering
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
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Recommendation systems analyze user preferences and recommend items to a user by predicting the user's preference for those items.Among various kinds of recommendation methods, collaborative filtering (CF) has been widely used and successfully applied to practical applications.However, collaborative filtering has two inherent problems: data sparseness and the cold-start problems.In this paper, we propose a method of integrating additional feature information of users and items into CF to overcome the difficulties caused by sparseness and improve the accuracy of recommendation. Several experimental results that show the effectiveness of the proposed method are also presented.