X. Deng, P. Jiang and X. Chen, "Multirelational Collaborative Filtering for Global Graph Neural Networks to Mine Evolutional Social Relations," in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2022.3229400. (JCR 1区)

Release time:2024-03-13

Journal:IEEE Transactions on Computational Social Systems

Abstract:Abstract—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.

Note:http://faculty.csu.edu.cn/dengxiaoheng/zh_CN/lwcg/10445/content/49288.htm

Translation or Not:no