Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation

Yuening Zhou, Yulin Wang, Qian Cui, Xinyu Guan, Francisco Cisternas
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引用次数: 0

Abstract

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.
用于价格敏感型下一篮子推荐的篮子增强型异质超图
下一篮子推荐(NBR)是一种新型推荐系统,它能预测用户可能一起购买的商品组合。现有的 NBR 模型往往忽略了一个关键因素,那就是价格,而且不能完全捕捉商品-篮子-用户之间的互动。为了解决这些局限性,我们提出了一种名为 "篮子增强动态异构超图(BDHH)"的新方法。BDHH 利用异构多关系图来捕捉商品特征之间错综复杂的关系,其中价格是一个关键因素。此外,我们的方法还包括一个篮子引导的动态增强网络,可以动态增强商品-篮子-用户之间的互动。在真实世界数据集上的实验证明,BDHH 显著提高了推荐的准确性,提供了对用户行为更全面的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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