Neighborhood-enhanced transfer learning for one-class collaborative filtering

Recommender systems have become more prevalent in recent years for providing users with personalized services such as movie recommendation and news recommendation. In real-world scenarios, they are naturally thought of as one-class collaborative filtering (OCCF) problems because most behavioral data are users’ interaction records, e.g., browses or clicks, which are referred to as one-class feedback or implicit feedback. In these problems, the sparsity of observed feedback and the ambiguity of unobserved feedback make it difficult to capture users’ true preferences. In order to alleviate that, two well-known approaches have been proposed, including factorization-based methods aiming to learn the relationships between users and items via latent factors, and neighborhood-based methods focusing on similarities between users or items. However, these two types of approaches are rarely studied in one single framework or solution for OCCF. In this paper, we propose a novel transfer learning solution, i.e., transfer by neighborhood-enhanced factorization (TNF), which combines these two approaches in a unified framework. Specifically, we extract the local knowledge of the neighborhood information among users, and then transfer it to a global preference learning task in an enhanced factorization-based framework. Our TNF is expected to exploit the local knowledge in a global learning manner well. Extensive empirical studies on five real-world datasets show that our proposed solution can perform significantly more accurate than the state-of-the-art methods.