GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
User preference representation based on psychometric models
ADC '11 Proceedings of the Twenty-Second Australasian Database Conference - Volume 115
Collaborative filtering using multidimensional psychometrics model
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Memory-based collaborative filtering is one of the most popular methods used in recommendation systems. It predicts a user’s preference based on his or her similarity to other users. Traditionally, the Pearson correlation coefficient is often used to compute the similarity between users. In this paper we develop novel memory-based approach that incorporates user’s latent interest. The interest level of a user is first estimated from his/her ratings for items through a latent trait model, and then used for computing the similarity between users. Experimental results show that the proposed method outperforms the traditional memory-based one.