Multi-Order Attentive Ranking Model for Sequential Recommendation


  • Lu Yu King Abdullah University of Science and Technology
  • Chuxu Zhang University of Notre Dame
  • Shangsong Liang Sun Yat-sen University
  • Xiangliang Zhang King Abdullah University of Science and Technology



In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interaction that can stand out the transition order between a pair of items, or union-level relation between a set of items and single one. However, most of existing work only captures one of them from a single view, especially on modeling the individual-level interaction. In this paper, we propose a Multi-order Attentive Ranking Model (MARank) to unify both individual- and union-level item interaction into preference inference model from multiple views. The idea is to represent user’s short-term preference by embedding user himself and a set of present items into multi-order features from intermedia hidden status of a deep neural network. With the help of attention mechanism, we can obtain a unified embedding to keep the individual-level interactions with a linear combination of mapped items’ features. Then, we feed the aggregated embedding to a designed residual neural network to capture union-level interaction. Thorough experiments are conducted to show the features of MARank under various component settings. Furthermore experimental results on several public datasets show that MARank significantly outperforms the state-of-the-art baselines on different evaluation metrics. The source code can be found at




How to Cite

Yu, L., Zhang, C., Liang, S., & Zhang, X. (2019). Multi-Order Attentive Ranking Model for Sequential Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5709-5716.



AAAI Technical Track: Machine Learning