考虑价格因素的动态异构超图模型，提升商品组合推荐准确率
Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation
- 构建异构多关系超图，融合价格信息建模商品-购物篮-用户关系
- 动态增强网络有效捕捉用户购买组合的上下文依赖性
- 在真实数据集上显著优于现有方法，适合价格敏感推荐场景
下一购物篮推荐（NBR）是一种新型推荐系统，旨在预测用户可能一起购买的商品组合。现有模型常忽略价格这一关键因素，且未能充分捕捉商品-购物篮-用户间的复杂交互。为此，我们提出一种新方法——篮子增强型动态异构超图（BDHH）。BDHH利用异构多关系图捕捉商品特征间的复杂关系，并将价格作为核心因素；同时引入篮子引导的动态增强网络，动态强化商品-购物篮-用户之间的交互。在真实数据集上的实验表明，BDHH显著提升了推荐准确率，提供了对用户行为更全面的理解。
原文摘要 · Abstract (English)
Next Basket Recommendation (NBR) is a new type of recommender system that predicts combinations of items users are likely to purchase together. Existing NBR models often overlook a crucial factor, which is price, and do not fully capture item-basket-user interactions. To address these limitations, we propose a novel method called Basket-augmented Dynamic Heterogeneous Hypergraph (BDHH). BDHH utilizes a heterogeneous multi-relational graph to capture the intricate relationships among item features, with price as a critical factor. Moreover, our approach includes a basket-guided dynamic augmentation network that could dynamically enhances item-basket-user interactions. Experiments on real-world datasets demonstrate that BDHH significantly improves recommendation accuracy, providing a more comprehensive understanding of user behavior.
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