CABRec: A Category-Aware Bundle Recommendation Model
Mengmeng Li, Jinlong Tian, Hongmei Li, Qiyuan Zhang, Xianglong Li, Xinhai Xu · 2025
The surge of multimedia content-spanning images, audio, video, and text-on digital platforms has heightened the need for sophisticated bundle recommendation techniques to curate cohesive item sets that resonate with users' preferences amidst rich media environments. Bundle recommendation aims to recommend thematically related item sets to users based on their preferences by simulating their cognitive decision-making process. It not only enhances user experience but also significantly boosts merchant profits, making it an increasingly crucial research area. However, existing studies often face cognitive limitations in modeling user preferences. They typically adopt a unified rule to learn user and bundle representations from items, failing to capture the diversity of bundling strategies and the underlying factors of user preferences. Therefore, we propose a Category-Aware Bundle Recommendation model, called CABRec. Specifically, CABRec learns user preferences from three views: User-Bundle (UB), User-Item (UI) and Bundle-Item (BI). From the UB view, we model users' direct preferences for bundles based on user-bundle interactions. From the UI and BI views, we model users' indirect preferences for bundles by constructing item-bundle layer and item-user layer to learn category-aware representations of bundles and users, respectively. Then, to capture the preference variations of different users on different views, we propose a user-specific prediction layer to learn a set of personalized preference weights for each user. Finally, we apply contrastive learning across these three views to utilize the complementary information they provide, enabling the model to learn more robust and generalizable representations. Extensive experiments on three datasets demonstrate that our method surpasses the strongest baseline, achieving a 2.33% ~ 17.02% improvement on recall.