A Matrix Factorization & amp; Clustering Based Approach for Transfer Learning

Abstract

Recommender systems that make use of collaborative filtering tend to suffer from data sparsity as the number of items rated by the users are very small as compared to the very large item space. In order to alleviate it, recently transfer learning (TL) methods have seen a growing interest wherein data is considered from multiple domains so that ratings from the first (source) domain can be used to improve the prediction accuracy in the second (target) domain. In this paper, we propose a model for transfer learning in collaborative filtering wherein the latent factor model for the source domain is obtained through Matrix Factorization (MF). User and Item matrices are combined in a novel way to generate cluster level rating pattern and a Code Book Transfer (CBT) is used for transfer of information from source to the target domain. Results from experiments using benchmark datasets show that our model approximates the target matrix well.