M: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation

Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning

Abstract: 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 novel mixed model with preferences, popularities and transitions ( $\mathop {\mathtt {M^2}}\limits$ ) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users’ general preferences, 2) items’ global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, $\mathop {\mathtt {M^2}}\limits$ does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach ( $\mathop {\mathtt {ed\text{-}Trans}}\limits$ ) to better model the transition patterns among items. We compared $\mathop {\mathtt {M^2}}\limits$ with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that $\mathop {\mathtt {M^2}}\limits$ significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the $\mathop {\mathtt {ed\text{-}Trans}}\limits$ is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.