Bilateral Sequential Hypergraph Convolution Network for Reciprocal Recommendation

Jiaxing Chen, Hongzhi Liu, Hongrui Guo, Yingpeng Du, Zekai Wang, Yang Song, Zhonghai Wu

Abstract: Reciprocal recommendation is the core of many social websites like online recruitment and online dating. Most recently, graph neural networks have been exploited by few researchers for reciprocal recommendation. However, they tend to oversimplify the interactions between users, treating them as simple pairwise relationships, which overlooks the multidimensional relationships among users. Additionally, these methods fail to consider users’ historical interaction sequences and feedback behaviors, which makes it challenging to effectively capture the changes of user preferences over time.To address these issues, this study proposes a novel bilateral recommendation model based on sequential hypergraphs for reciprocal scenarios. Firstly, to capture the complex multidimensional relationships between bilateral users, we design a new data structure called bilateral sequential hypergraphs to capture the diverse relationships among users and to mine collaborative signals at the sequential level. Secondly, we propose corresponding bilateral sequential hypergraph convolution structures to learn the embedded representations of bilateral users. To adequately capture the changes in user preferences, the model incorporates position modeling and feedback behavior modeling within the proposed convolution strategy. Extensive experiments on several real-world datasets demonstrate that the proposed method consistently outperforms state-of-the-art methods.