Multi-Order Attentive Ranking Model for Sequential Recommendation

Lu Yu, Chuxu Zhang, Shangsong Liang, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

83 Scopus citations

Abstract

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 https://github.com/voladorlu/MARank.
Original languageEnglish (US)
Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence
Pages5709-5716
Number of pages8
DOIs
StatePublished - 2019

Bibliographical note

KAUST Repository Item: Exported on 2021-09-02
Acknowledged KAUST grant number(s): FCC/1/1976-24-01
Acknowledgements: This work was supported by the funding from King Abdullah University of Science and Technology (KAUST), under award number FCC/1/1976-24-01.

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