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

TL;DR

This paper introduces BDHH, a novel hypergraph-based model that incorporates price and dynamic interactions to improve next basket recommendation accuracy.

Contribution

It proposes a basket-augmented dynamic heterogeneous hypergraph model that captures complex item, basket, user relationships including price factors.

Findings

Significantly outperforms existing NBR models in accuracy

Effectively models price and interaction dynamics

Enhances understanding of user purchase behavior

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.