Cyclic Transfer Learning for Recommender Systems with Heterogeneous Feedbacks

Transfer learning uses auxiliary domains to help complete learning tasks of the target domain. However, the combination of recommendation and transfer learning often has two problems. One is that it’s difficult to find an auxiliary domain which is highly related to the target domain. The other is that useful information in auxiliary domains cannot be fully utilized. To make use of the knowledge in auxiliary domains as much as possible, this paper proposes a cyclic transfer learning method which can transfer the shared knowledge in the auxiliary domain and target domain multiple times. Combining this method with recommendation, this paper presents a recommendation framework based on heterogeneous feedbacks and cyclic transfer learning (HCTLRec). By studying the relationship between different behaviors of users, this paper proposes two specific recommendation algorithms which combine the novel framework with two auxiliary domains. One is to use users’ binary attitude information as an auxiliary domain to better represent users’ ratings. The other is to use users’ trust relationship as an auxiliary domain and make social recommendation. Experiments are carried out on two real-world datasets with trust relationship. The results show that recommendation quality of the two specific algorithms can achieve significant improvement compared with other state-of-the-art algorithms and can effectively relieve the cold-start problem.