Multibehavior Recommendation With Nonoverlapping Heterogeneous Graph Collaborative Filtering

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Guan Yuan, Peijin Yang, Zhuo Cai, Yuqing Du, Shaojie Qiao, Qiuyan Yan

Published: 2025, Last Modified: 23 Jan 2026IEEE Trans. Comput. Soc. Syst. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Recommender systems (RS) have found extensive applications in web and internet services, yielding significant advantages for both users and service providers, including domains such as E-commerce and Movie platforms, News portals and so on. As a crucial branch of recommendation services, multibehavior recommendation aims to leverage auxiliary behavior data (e.g., page-view and add-to-cart) to improve the recommendation performance on target behavior (e.g., purchase). Multibehavior recommendation align more closely with real-world scenarios, resulting greater research significance. However, there are two-fold issues existed in the present models that adversely affect the service quality of multibehavior RS: 1) the behavior overlap (i.e., the interaction records between identical user-item pairs co-existed in different behavior data) hinders accurate user preference learning; and 2) the commonalities of user preferences over items under different behaviors are not well explored, thereby making behavior intercorrelations not been fully captured. To handle these, we propose a nonoverlapping heterogeneous graph multibehavior recommendation (No-HGMR) model. To tackle the first problem, we erase the overlap between multitype behavior data to construct a nonoverlapping heterogeneous graph. In this graph, each specific user-item pair is connected by only one type of edge, mitigating confusion and enabling more accurate user preference learning. To solve the second problem, we construct correlation features based on user properties and item attributes. These features are then incorporated into the user preference learning procedure, facilitating the understanding of users’ common preference over items under different behaviors. Extensive experiments on real-world datasets verify the effectiveness of No-HGMR as compared to competitive state-of-the-art methods.