HED：基于超图增强双卷积网络的捆绑推荐模型

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Abstract:Bundle recommendation ranks sets of related items rather than isolated items. Its central challenge is to connect user preferences, item interactions, and bundle composition without losing the signals needed to rank bundles. We propose Hypergraph-Enhanced Dual Convolutional Neural Network (HED), which constructs a complete hypergraph containing user--bundle, user--item, and bundle--item interactions together with intra-user and intra-bundle relations. HED couples complete-hypergraph propagation with a user--bundle branch, allowing item-aware higher-order context to inform ranking while preserving recommendation-specific signals. On NetEase, HED-128 improves over the strongest baseline by 5.04--6.97% across the six reported metrics; on Youshu, HED-64 improves by 1.87--4.56%. Ablation results support the contributions of both the user--bundle branch and intra-type relations, and sensitivity analyses identify stable operating ranges for the main hyperparameters. We further quantify the computational trade-off of the complete hypergraph, including its memory cost. The evidence supports HED on the two evaluated bundle-recommendation datasets while making its resource limitations explicit. Code and datasets will be made available upon publication.

Subjects:	Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2312.11018 [cs.IR]
	(or arXiv:2312.11018v3 [cs.IR] for this version)
	https://doi.org/10.48550/arXiv.2312.11018 arXiv-issued DOI via DataCite

Submission history

From: Kangbo Liu [view email]
[v1] Mon, 18 Dec 2023 08:35:10 UTC (4,459 KB)
[v2] Tue, 10 Dec 2024 11:20:21 UTC (7,577 KB)
[v3] Tue, 6 Oct 2026 10:03:50 UTC (7,081 KB)