Leveraging implicit relations for recommender systems

Abstract

Collaborative filtering (CF) is one of the dominant techniques used in recommender systems. Most CF-based methods treat every user (or item) as an isolated existence, without explicitly modeling potential mutual relations among users (or items), which are latent in user-item interactions. In this paper, we design a novel strategy to mine user-user and item-item implicit relations and propose a natural way of utilizing the implicit relations for recommendation. Specifically, our method contains two major phases: neighbor construction and recommendation framework. The first phase constructs an implicit neighbor set for each user and item according to historical user-item interaction. In the second phase, based on the constructed neighbor sets, we propose a deep framework to generate recommendations. We conduct extensive experiments with four datasets on the movie, business, book, and restaurant recommendations and compare our methods with seven baselines, e.g., feature-based, neighborhood-based, and graph-based models. The experiment results demonstrate that our method achieves superior performance in rating prediction and top-k recommendation.