Next Basket Recommendation Model Based on Attribute-Aware Multi-Level Attention

Abstract

Next basket recommendation is a challenging problem, mainly due to the relationships among the items in a basket almost not being considered in current research. In this article, we address next basket recommendation with a novel deep learning architecture. In particular, we consider both the short-term user interests and the long-term user preferences, and we design a new attention that considers the relationships among the items in a basket. We extensively evaluated the proposed model on two benchmark data sets, the Ta-Feng and JingDong datasets. The experimental results show that the proposed model outperforms several state-of-the-art next basket recommendation models. In the experiments of the modules' effects, we also verify the effectiveness of each module. The model significantly improves the NDCG by 25.5 percentage points and 5 percentage points when compared with the uncompleted network model on the JingDong and Ta-Feng datasets, respectively; while in terms of the F1, the performance is improved by 14.6 and 23.4 percentage points, respectively.