Bundle recommender systems merely learn from existing bundles, but obtaining large-scale, high-quality bundle datasets remains a challenge, especially for platforms newly adopting bundle services. Bundle construction is the task of automatically selecting a set of compatible items to form a coherent bundle, a vital step before making recommendations on bundle-aware platforms. Groundbreaking work on bundle construction, like CLHE, has been designed solely on user-item interaction and self-attention modules to learn item/bundle representations. These techniques fall short of the standards for coherent bundles in real-world applications, where the relation among the semantic information of items should be considered more thoroughly. To address these challenges, we explicitly leverage category-wise information and employ cross-modal fusion to enhance item representations. By doing so, we propose Caro: Dual-Enhanced Item Representation for Bundle Construction via Category-Wise and Cross-Modality Learning. Caro captures the inherent relationships between items within analogous categories, improving bundle coherence. It comprises three main components: (1) cross-modality enhanced item representation, (2) category-enhanced item representation, and (3) bundle contrastive learning. Extensive experiments and detailed analyzes using multiple real-world datasets demonstrate that our method outperforms existing state-of-the-art techniques and provides valuable insight into the bundle construction problem. Notably, Caro achieves a 5-8% higher Recall@20 than the strongest baseline, underscoring its performance gains through dual category-wise and cross-modal enhancements. [...]

Bundle recommendation aims to enhance business profitability and user convenience by suggesting a set of interconnected items. In real-world scenarios, leveraging the impact of asymmetric item affiliations is crucial for effective bundle modeling and understanding user preferences. To address this, we present BunCa, a novel bundle recommendation approach employing item-level causation-enhanced multi-view learning. BunCa provides comprehensive representations of users and bundles through two views: the Coherent View, leveraging the Multi-Prospect Causation Network for causation-sensitive relations among items, and the Cohesive View, employing LightGCN for information propagation among users and bundles. Modeling user preferences and bundle construction combined from both views ensures rigorous cohesion in direct user-bundle interactions through the Cohesive View and captures explicit intents through the Coherent View. Simultaneously, the integration of concrete and discrete contrastive learning optimizes the consistency and self-discrimination of multi-view representations. Extensive experiments with BunCa on three benchmark datasets demonstrate the effectiveness of this novel research and validate our hypothesis.