Same Items, Different Roles: Context-Aware Facet Disentanglement Graph Network for Discriminative Bundle Recommendation

Discriminative bundle recommendation aims to rank predefined item collections (i.e., bundles) for users. Multi-view graph methods integrate user-item, user-bundle, and bundle-item relations. However, they do not use co-member-conditioned facet roles and explicit bundle structure to dynamically select from history-derived interests when ranking candidate bundles. To address this gap, we propose a context-aware facet disentanglement graph network for discriminative bundle recommendation, namely CFDGN. First, a co-member-conditioned router combines intrinsic item facets with bundle context to infer bundle-specific roles. Second, a role-aware component summarizes role-derived bundle structure as three unlabeled soft states (i.e., Similar, Complementary, and Noise). Third, historical bundles provide candidate-independent interest values for each user. The current candidate selects their mixture, which is then evaluated through four structural matching paths. A positive pointwise mutual information (PPMI) prior derived exclusively from training-observed bundles supplies auxiliary relation evidence. A calibrated structural residual augments the backbone MultiCBR's collaborative score. Experiments on three benchmark datasets show that CFDGN exceeds eleven competing baselines on both Recall and NDCG. Its relative Recall@20 gains over MultiCBR are 3.8%, 1.7%, and 8.3%, respectively.