Divide-and-Conquer: Cold-Start Bundle Recommendation via Mixture of Diffusion Experts

Cold-start bundle recommendation focuses on modeling new bundles with insufficient information to provide recommendations. Advanced bundle recommendation models usually learn bundle representations from multiple views (e.g., user-bundle interaction views) at both bundle and item levels. Consequently, the cold-start problem for bundles is more challenging than that for traditional items due to the dual-level multi-view complexity. For cold-start bundle recommendation, we propose a novel Mixture of Diffusion Experts (MoDiffE) framework, which employs a divide-and-conquer strategy and consists of three parts: (1) Division : The bundle cold-start problem is divided into view-specific but unified sub-problems: the poor representation of feature-missing bundles in prior-embedding models. (2) Conquest : Diffusion models uniformly solve all sub-problems by directly generating diffusion representations without depending on specific features. (3) Combination : A cold-aware hierarchical Mixture of Experts (MoE) is employed to adaptively combine results of the sub-problems into final recommendations. Additionally, MoDiffE proposes a cold-start gating augmentation method to enable gating for cold bundles. In experiments on three real-world datasets, MoDiffE significantly outperforms existing solutions in cold-start bundle recommendation. It achieves up to a 0.1027 Recall@20 improvement in cold-start scenarios and up to a 47.43% relative improvement in all-bundle scenarios.