Incorporating Co-purchase Correlation for Next-basket Recommendation

Abstract

Next-basket recommendation (NBR) aims to recommend a set of items that users would most likely purchase together. Existing approaches use deep learning to capture basket-level preference and traditional statistical methods to model user behavior sequences. However, these methods neglect the correlation of co-purchase items among users. We, therefore, propose a novel model that incorporates Co-purchase Correlation with Bidirectional Transformer (CCBT) to enhance item representation by exploiting the correlation among users' baskets. The results of experiments conducted on four real-world datasets demonstrate the proposed model outperforms state-of-the-art NBR methods. The relative improvement for Recall@20 ranges from 11% to 27%.