M2pht: Mixed Models with Preferences and Hybrid Transitions for Next-Basket Recommendation

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a new mixed model with preferences and hybrid transitions for the next-basket recommendation problem. This method explicitly models three important factors: 1) users' general preferences; 2) transition patterns among items and 3) transition patterns among baskets. We compared this method with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets. Our experimental results demonstrate that our method significantly outperforms the state-of-the-art methods on all the datasets. We also conducted a comprehensive ablation study to verify the effectiveness of the different factors.