Dual Sequential Recommendation Integrating High-order Collaborative Relations via Graph Attention Networks

Abstract

Sequential recommendation aims to predict users' preferences in the future based on the interactions that they have done recently. Since users only interact with a small part of items, there is a problem of data sparsity in real-world datasets. Some studies leverage collaborative filtering (CF) to model collaborative relations to alleviate data sparsity. However, CF focuses on modeling first-order relations and ignores high-order collaborative relations. Meanwhile, most of the models assume the attributes of items are static, and only consider users' dynamics. In this paper, we propose Dual Sequential Recommendation with Collaborative Relations (DSRCR) which aggregates high-order collaborative features on the user-user graph and item-item graph via graph attention network as the input of dual sequential models and utilizes the attention-based recurrent neural network to model both user-side and item-side sequences. The contribution of different hidden states to user preferences or item attributes can be captured by the attention mechanism. Each user (item) is represented as the composition of the embedding with high-order collaborative features and the embedding that obtains from sequential models. Extensive experiments on three datasets demonstrate the superiority of our proposed model compared with other state-of-the-art baselines.