Collaborative filtering recommendation algorithm optimization based on latent factor model clustering

Collaborative filtering (CF) is the most popular recommendation algorithm, which predicts unknown ratings through historic data. However, the traditional collaborative filtering algorithm only considers the individual user's historical behavior of items, without considering the users' preferences and attributes. In this paper, a collaborative filtering algorithm based on latent factor model clustering (CF-LFMC) is proposed. Firstly, the user's score matrix is decomposed into two matrices by the latent factor model (LFM) and gets the user-class matrix. Secondly, the user-class matrix is decomposed into many clusters by the clustering algorithm, which presents users' hidden attributes. Finally, based on users' hidden attributes and users' history score record, the similarity of items is calculated and item scores can be predicted. Through experiments on the MovieLens dataset, it shows that the proposed algorithm can reduce the mean absolute error and improve the accuracy of recommendation.