Adaptive attention-aware gated recurrent unit for sequential recommendation

Anjing Luo, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Zhixu Li, Lei Zhao, Victor S. Sheng, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Due to the dynamic and evolutionary characteristics of user interests, sequential recommendation plays a significant role in recommender systems. A fundamental problem in the sequential recommendation is modeling dynamic user preference. Recurrent Neural Networks (RNNs) are widely adopted in the sequential recommendation, especially attention-based RNN becomes the state-of-the-art solution. However the existing fixed attention mechanism is insufficient to model the dynamic and evolutionary characteristics of user sequential preferences. In this work, we propose a novel solution, Adaptive Attention-Aware Gated Recurrent Unit (3AGRU), to learn adaptive user sequential representations for sequential recommendation. Specifically, we adopt an attention mechanism to adapt the representation of user sequential preference, and learn the interaction between steps and items from data. Moreover, in the first level of 3AGRU, we construct adaptive attention network to describe the relevance between input and the candidate item. In this way, a new input based on adaptive attention can reflect users’ diverse interests. Then, the second level of 3AGRU applies adaptive attention network to hidden state level to learn a deep user representation which is able to express diverse interests of the user. Finally, we evaluate the proposed model using three real-world datasets from various application scenarios. Our experimental results show that our model significantly outperforms the state-of-the-art approaches on sequential recommendation.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings
EditorsJun Yang, Yongxin Tong, Juggapong Natwichai, Joao Gama, Guoliang Li
Number of pages16
ISBN (Print)9783030185787
StatePublished - 2019
Event24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand
Duration: Apr 22 2019Apr 25 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11447 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
CityChiang Mai


  • Adaptive attention mechanism
  • GRU
  • Recommender system


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