Using latent class models for neighbors selection in collaborative filtering

Abstract

Collaborative filtering is becoming a popular technique for reducing information overload. However, most of current collaborative filtering algorithms have three major limitations: accuracy, data sparsity and scalability. In this paper, we propose a new collaborative filtering algorithm to solve the problem of data sparsity and improve the prediction accuracy. If the rated items amount of a user is less than some threshold, the algorithm utilizes the output of latent class models for neighbors selection, then uses the neighborhood-based method to produce the prediction of unrated items, otherwise it predicts the rating using the STIN1 method. Our experimental results show that our algorithm outperforms the conventional neighborhood-based method and the STIN1 method. © Springer-Verlag Berlin Heidelberg 2005.