Multi-level User Interest and Multi-intent Fusion for Next Basket Recommendation

Next basket recommendation aims to recommend the next basket of items that users may be interested in based on the basket sequence of user historical interactions. The existing next-basket recommendation algorithms fail to adequately disentangle multi-intents within baskets and consider user interests or intents from only a single level, thereby     resulting in suboptimal recommendation performance. To address the limitations, this paper proposes a multi-level user  interest and multi-intent fusion model (MLIMI) for next-basket recommendation. This model separately considers user   interests and multi-intents from multiple levels.  Firstly, a global-level user-item interaction graph is constructed. Considering that user behavior changes over time, a long and short-term time decay weight is designed to balance the importance of the interaction items, and then the user’s dynamic interests are learnt through graph convolution networks. Secondly, a    local-level basket-item graph is constructed to learn the disentangled multi-intents within baskets via a graph disentangled network, and subsequently the multi-intents are encoded via a multi-head self-attention layer to obtain the final intent representations. A cross-level contrastive learning paradigm is also designed to combine item representations from different levels in order to enhance the semantic information between items at different levels. Finally, user interests and intents from different levels are fused in the predict layer for the next basket of predictions. Comparative experiments with mainstream models such as MITGNN, TAIW and MINN on two public benchmark datasets, TaFeng and Dunnhumby, show that MLIMI outperforms many current baseline models.