Dynamic Memory based Attention Network for Sequential Recommendation

Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren ZHOU, Xia Hu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Sequential recommendation has become increasingly essential in various online services. It aims to model the dynamic preferences of users from their historical interactions and predict their next items. The accumulated user behavior records on real systems could be very long. This rich data brings opportunities to track actual interests of users. Prior efforts mainly focus on making recommendations based on relatively recent behaviors. However, the overall sequential data may not be effectively utilized, as early interactions might affect users' current choices. Also, it has become intolerable to scan the entire behavior sequence when performing inference for each user, since real-world system requires short response time. To bridge the gap, we propose a novel long sequential recommendation model, called Dynamic Memory-based Attention Network (DMAN). It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users. To improve memory fidelity, DMAN dynamically abstracts each user's long-term interest into its own memory blocks by minimizing an auxiliary reconstruction loss. Based on the dynamic memory, the user's short-term and long-term interests can be explicitly extracted and combined for efficient joint recommendation. Empirical results over four benchmark datasets demonstrate the superiority of our model in capturing long-term dependency over various state-of-the-art sequential models.
Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
Pages4384-4392
Publication statusPublished - 18 May 2021
Event35th AAAI Conference on Artificial Intelligence -
Duration: 2 Feb 20219 Feb 2021

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence

Conference

Conference35th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI
Period2/02/219/02/21

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