Yuening Zhou, Yulin Wang, Qian Cui, Xinyu Guan, Francisco Cisternas
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Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation
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.