MBN: Towards Multi-Behavior Sequence Modeling for Next Basket Recommendation

Abstract

Next basket recommendation aims at predicting the next set of items that a user would likely purchase together, which plays an important role in e-commerce platforms. Unlike conventional item recommendation, the next basket recommendation focuses on capturing item correlations among baskets and learning the user’s temporal interest from the past purchasing basket sequence. In practice, most users interact with items in various kinds of behaviors. The multi-behavior data sheds light on user’s potential purchasing intention and resolves noisy signals from accidentally purchased items. In this article, we conduct an empirical study on real datasets to exploit the characteristics of multi-behavior data and confirm its positive effects on next basket recommendation. We develop a novel Multi-Behavior Network (MBN) model that captures item correlations and acquires meta-knowledge from multi-behavior basket sequences effectively. MBN employs the meta multi-behavior sequence encoder to model temporal dependencies of each individual behavior and extract meta-knowledge across different behaviors. Furthermore, we design the recurring-item-aware predictor in MBN to realize the high degree of the repeated occurrences of items, leading to better recommendation performance. We conduct extensive experiments to evaluate the performance of our proposed MBN model using real-world multi-behavior data. The results demonstrate the superior recommendation performance of MBN compared with various state-of-the-art methods.