Self-Supervised Dual-Channel Attentive Network for Session-based Social Recommendation

The task of Session-based Social Recommendation (SSR) aims to utilize the social networks to make recommendations in session-based scenarios. Existing SSR methods mainly focused on using graph networks to capture complex item transition patterns, ignoring the sequential information. Few studies combined two aspects of features to enhance session preferences, resulting in information loss. Besides, modeling the entire session that some items are invalid or repeatedly clicked will interfere with the results. In this paper, to address the information loss issue in SSR, we propose a novel Dual-Channel Attentive Network (DCAN) to leverage both sequential infor-mation and complex item transitions. Specifically, we construct one channel by a light graph attention layer to capture item transitions, and we elaborate a concise attention-based layer to build the other channel to learn sequential information. To solve the invalid or repeatedly clicked problem in the session, we introduce new self-supervised learning (SSL) learning method, which allows model learning to distinguish and discard these items. However, the effect of SSL in SSR has not been investigated yet. Besides, these studies require negative sampling, which makes its performance depend on negative sampling strategies. Then, we investigate the effect of adding existing SSL frameworks in DCAN, but it has not achieved good results. Besides, we propose a novel SSL framework that does not require negative sampling for SSR, denoted as Positive sampling SSL (PSSL). Furthermore, we combined DCAN and PSSL to make more accurate recommendations, denoted as DCAN - PSSL. Extensive experiments on three public benchmark datasets demonstrate that both DCAN and DCAN - PSSL consistently outperform the state-of-the-art models.