Leveraging implicit relations for recommender systems

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. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

A novel recommendation method is proposed in this study, which enhances recommendation performance by mining implicit relations between users and items. Experimental results demonstrate that the method achieves superior performance in rating prediction and Top-k recommendation.