Spatio-Temporal Self-Attention Network for Next POI Recommendation

Jiacheng Ni, Pengpeng Zhao, Jiajie Xu, Junhua Fang, Zhixu Li, Xuefeng Xian, Zhiming Cui, Victor S. Sheng

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

Abstract

Next Point-of-Interest (POI) recommendation, which aims to recommend next POIs that the user will likely visit in the near future, has become essential in Location-based Social Networks (LBSNs). Various Recurrent Neural Network (RNN) based sequential models have been proposed for next POI recommendation and achieved state-of-the-art performance, however RNN is difficult to parallelize which limits its efficiency. Recently, Self-Attention Network (SAN), which is purely based on the self-attention mechanism instead of recurrent modules, improves both performance and efficiency in various sequential tasks. However, none of the existing self-attention networks consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Self-Attention Network (STSAN), which combines self-attention mechanisms with spatio-temporal patterns of users’ check-in history. Specifically, time-specific weight matrices and distance-specific weight matrices through a decay function are used to model the spatio-temporal influence of POI pairs. Moreover, we introduce a simple but effective way to dynamically measure the importances of spatial and temporal weights to capture users’ spatio-temporal preferences. Finally, we evaluate the proposed model using two real-world LBSN datasets, and the experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.

Original languageEnglish
Title of host publicationWeb and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Proceedings
EditorsXin Wang, Rui Zhang, Young-Koo Lee, Le Sun, Yang-Sae Moon
PublisherSpringer Science and Business Media Deutschland GmbH
Pages409-423
Number of pages15
ISBN (Print)9783030602581
DOIs
StatePublished - 2020
Event4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020 - Tianjin, China
Duration: Sep 18 2020Sep 20 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12317 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020
Country/TerritoryChina
CityTianjin
Period09/18/2009/20/20

Keywords

  • Point-of-Interest
  • Recommender system
  • Self-Attention Network

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