Multirelational Collaborative Filtering for Global Graph Neural Networks to Mine Evolutional Social Relations

Due to the unstable and complex social network environment, the sole user-item interaction data become insufficient for generating precise recommendations. However, too much emphasis on user-item interactions prevents the discovery of internal connections among them, such as trustworthy user relations. In this work, we have integrated the collaborative and the sequential relations into an end-to-end graph neural network (GNN) simultaneously and proposed a novel framework, namely multirelational collaborative filtering (MRCF), to explore the evolutional social relations. MRCF mainly consists of two components: relational GNN (RGNN) and simple dot-product attention (SDPA), where RGNN is used to capture not only the collaborative but also the sequential relationship from reliable user-item historical interactions through the graph representation, while SDPA can further concentrate on the dominated interaction sequences between users and items. Moreover, a negative sampling method based on user interest is proposed to help train our model. Extensive experiments on three real-world datasets show that the proposed model performs competitively with other state-of-the-art methods in CF.