Deep Adversarial Social Recommendation

TL;DR

This paper introduces DASO, a deep adversarial framework for social recommendation that uses bidirectional mapping and adversarial learning to better capture user behavior across social and item domains, improving recommendation accuracy.

Contribution

It proposes a novel deep adversarial framework with bidirectional mapping to address heterogeneity in user representations across social and item domains.

Findings

DASO outperforms existing methods on real-world datasets.

The bidirectional adversarial approach effectively transfers user information.

The framework improves recommendation performance by capturing domain-specific user behaviors.

Abstract

Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life. The majority of existing social recommendation methods unify user representation for the user-item interactions (item domain) and user-user connections (social domain). However, it may restrain user representation learning in each respective domain, since users behave and interact differently in the two domains, which makes their representations to be heterogeneous. In addition, most of traditional recommender systems can not efficiently optimize these objectives, since they utilize negative sampling technique which is unable to provide enough informative guidance towards the training during the optimization process. In this paper, to address the aforementioned challenges, we propose a novel deep adversarial social recommendation framework DASO. It adopts a bidirectional mapping method to transfer users' information between social domain and item domain using adversarial learning. Comprehensive experiments on two real-world datasets show the effectiveness of the proposed framework.