User profile as a bridge in cross-domain recommender systems for sparsity reduction

Abstract

In the past two decades, recommender systems have been successfully applied in many e-commerce companies. One of the promising techniques to generate personalized recommendations is collaborative filtering. However, it suffers from sparsity problem. Alleviating this problem, cross-domain recommender systems came into existence in which transfer learning mechanism is applied to exploit the knowledge from other related domains. While applying transfer learning, some information should overlap between source and target domains. Several attempts have been made to enhance the performance of collaborative filtering with the help of other related domains in cross-domain recommender systems framework. Although exploiting the knowledge from other domains is still challenging and open problem in recommender systems. In this paper, we propose a method namely User Profile as a Bridge in Cross-domain Recommender Systems (UP-CDRSs) for transferring knowledge between domains through user profile. Firstly, we build a user profile using demographical information of a user, explicit ratings and content information of user-rated items. Thereafter, the probabilistic graphical model is employed to learn latent factors of users and items in both domains by maximizing posterior probability. At last prediction on unrated item is estimated by an inner product of corresponding latent factors of users and items. Validating of our proposed UP-CDRSs method, we conduct series of experiments on various sparsity levels using cross-domain dataset. The results demonstrate that our proposed method substantially outperforms other without and with transfer learning methods in terms of accuracy.