Sequential recommender system based on hierarchical attention network

Haochao YING, Fuzhen ZHUANG, Fuzheng ZHANG, Yanchi LIU, Guandong XU, Xing XIE, Hui XIONG, Jian WU

Research output: Chapter in Book/Report/Conference proceedingChapters

286 Citations (Scopus)

Abstract

With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or itemitem interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones. Copyright © 2018 International Joint Conferences on Artificial Intelligence.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
EditorsJérôme LANG
Place of PublicationVienna, Austria
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3926-3932
ISBN (Electronic)9780999241127
DOIs
Publication statusPublished - 2018

Citation

Ying, H., Zhuang, F., Zhang, F., Liu, Y., Xu, G., Xie, X., Xiong, H., & Wu, J. (2018). Sequential recommender system based on hierarchical attention network. In J. Lang (Ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) (pp. 3926-3932). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/546

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