Memory Augmented Hierarchical Attention Network for Next Point-of-Interest Recommendation

Chenwang Zheng, Dan Tao, Jiangtao Wang, Lei Cui, Wenjie Ruan, Shui Yu

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Next point-of-interest (POI) recommendation has been an important task for location-based intelligent services. However, the application of such promising technique is still limited due to the following three challenges: 1) the difficulty of capturing complicated spatiotemporal patterns of user movements; 2) the hardness of modeling fine-grained long-term preferences of users; and 3) the effective learning of interaction between long- and short-term preferences. Motivated by this, we propose a memory augmented hierarchical attention network (MAHAN), which considers both short-term check-in sequences and long-term memories. To capture the complicated interest tendencies of users within a short-term period, we design a spatiotemporal self-attention network (ST-SAN). For long-term preferences modeling, we employ a memory network to maintain fine-grained preferences of users and dynamically operate them based on users' constantly updated check-ins. Moreover, we first employ a coattention network/mechanism to integrate the proposed ST-SAN and memory network, which can fully learn the dynamic interaction between long- and short-term preferences. Our extensive experiments on two publicly available data sets demonstrate the effectiveness of MAHAN.

Original languageEnglish
Article number9262922
Pages (from-to)489-499
Number of pages11
JournalIEEE Transactions on Computational Social Systems
Issue number2
Early online date18 Nov 2020
Publication statusPublished - Apr 2021

Bibliographical note

Funding Information:
Manuscript received June 30, 2020; revised October 5, 2020; accepted October 25, 2020. Date of publication November 18, 2020; date of current version April 1, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61872027. The work of Shui Yu was supported in part by Australia Australian Research Council (ARC) under Grant DP180102828 and Grant DP200101374. The work of Lei Cui was supported in part by Australia ARC under Grant DE180100950. (Corresponding author: Dan Tao.) Chenwang Zheng and Dan Tao are with the School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China (e-mail:

Publisher Copyright:
© 2014 IEEE.

Copyright 2021 Elsevier B.V., All rights reserved.


  • Coattention
  • dynamic preference
  • memory network
  • point-of-interest (POI) recommendation
  • self-attention

ASJC Scopus subject areas

  • Modelling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction


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