Existing studies on bundle construction have relied merely on user feedback via bipartite graphs or enhanced item representations using semantic information. These approaches fail to capture elaborate relations hidden in real-world bundle structures, resulting in suboptimal bundle representations. To overcome this limitation, we propose RaMen, a novel method that provides a holistic multi-strategy approach for bundle construction. RaMen utilizes both intrinsic (characteristics) and extrinsic (collaborative signals) information to model bundle structures through Explicit Strategy-aware Learning (ESL) and Implicit Strategy-aware Learning (ISL). ESL employs task-specific attention mechanisms to encode multi-modal data and direct collaborative relations between items, thereby explicitly capturing essential bundle features. Moreover, ISL computes hyperedge dependencies and hypergraph message passing to uncover shared latent intents among groups of items. Integrating diverse strategies enables RaMen to learn more comprehensive and robust bundle representations. Meanwhile, Multi-strategy Alignment & Discrimination module is employed to facilitate knowledge transfer between learning strategies and ensure discrimination between items/bundles. Extensive experiments demonstrate the effectiveness of RaMen over state-of-the-art models on various domains, justifying valuable insights into complex item set problems.

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. [...]