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.
Cite
@article{arxiv.2409.11695,
title = {Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation},
author = {Yuening Zhou and Yulin Wang and Qian Cui and Xinyu Guan and Francisco Cisternas},
journal= {arXiv preprint arXiv:2409.11695},
year = {2024}
}