Virtual games have become a prominent cultural, social, and economic phenomenon, captivating millions of players globally, which stimulates intelligent services, like recommendations, with strong real evidence of enabling platforms to optimize operational management, enhance user satisfaction, and drive revenue growth. Even though traditional recommender systems that rely on modeling user preferences have achieved great success in commerce, they commonly model users’ purchase behavior with a unified preference representation, which has limitations for capturing users’ diverse intrinsic purchase motivations. In this paper, we therefore conduct a study that focuses on designing a recommender system for virtual games. We argue that purchases in virtual games are mainly motivated by both rational needs and emotional needs, with the former reflecting a player’s practical need to improve their ability for a specific game campaign (e.g., attack, defense) and the latter being more of a psychological preference (e.g., color, style). We design a framework called RERec that discriminately learns representations of the two types of motivations via distinct architectures and features a better-supervised optimization that is oriented toward the entire process rather than specific items. For its rational needs module, RERec employs a unique time-gating mechanism to perceive the temporal impacts of campaigns on players’ volatile rational needs. A heterogeneous item-taxonomy graph is also used as prior knowledge of similarity among items to accelerate convergence. On the other hand, because a player’s emotional preferences are relatively stable over a short period, RERec employs a hierarchical attention mechanism to capture the comprehensiveness and focus of the player’s emotional needs. RERec also incorporates player attributes to enhance the personalized emotional need representations. Extensive experiments and analyses on large-scale real-world data sets from both a well-known game company and the public benchmarks fully demonstrate RERec’s superiority in specific respects as well as its effectiveness. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This study was partially funded by the National Natural Science Foundation of China Grants 72101176 and 72471165 and Emerging Frontiers Cultivation Program of Tianjin University Interdisciplinary Center. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2025.1179 . The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2025.1179 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1179 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
Zhao et al. (Tue,) studied this question.