User’s Latent Interest-Based Collaborative Filtering

Abstract

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.