TY - JOUR AU - Yu, Lu AU - Zhang, Chuxu AU - Liang, Shangsong AU - Zhang, Xiangliang PY - 2019/07/17 Y2 - 2024/03/28 TI - Multi-Order Attentive Ranking Model for Sequential Recommendation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v33i01.33015709 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4516 SP - 5709-5716 AB - <p>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 <em>Multi-order Attentive Ranking Model</em> (<em>MARank</em>) 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 <em>residual neural network</em> to capture union-level interaction. Thorough experiments are conducted to show the features of <em>MARank</em> under various component settings. Furthermore experimental results on several public datasets show that <em>MARank</em> significantly outperforms the state-of-the-art baselines on different evaluation metrics. The source code can be found at https://github.com/voladorlu/MARank.</p> ER -