Multi-view Multi-aspect Neural Networks for Next-basket Recommendation

Next-basket recommendation (NBR) is a type of recommendation that aims to recommend a set of items to users according to their historical basket sequences. Existing NBR methods suffer from two limitations: (1) overlooking low-level item correlations, which results in coarse-grained item representation; and (2) failing to consider spurious interests in repeated behaviors, leading to suboptimal user interest learning. To address these limitations, we propose a novel solution named Multi-view Multi-aspect Neural Recommendation (MMNR) for NBR, which first normalizes the interactions from both the user-side and item-side, respectively, aiming to remove the spurious interests, and utilizes them as weights for items from different views to construct differentiated representations for each interaction item, enabling comprehensive user interest learning. Then, to capture low-level item correlations, MMNR models different aspects of items to obtain disentangled representations of items, thereby fully capturing multiple user interests. Extensive experiments on real-world datasets demonstrate the effectiveness of MMNR, showing that it consistently outperforms several state-of-the-art NBR methods.