TY - GEN
T1 - Spatio-Temporal Self-Attention Network for Next POI Recommendation
AU - Ni, Jiacheng
AU - Zhao, Pengpeng
AU - Xu, Jiajie
AU - Fang, Junhua
AU - Li, Zhixu
AU - Xian, Xuefeng
AU - Cui, Zhiming
AU - Sheng, Victor S.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Point-of-Interest
KW - Recommender system
KW - Self-Attention Network
UR - http://www.scopus.com/inward/record.url?scp=85093941851&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60259-8_30
DO - 10.1007/978-3-030-60259-8_30
M3 - Conference contribution
AN - SCOPUS:85093941851
SN - 9783030602581
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 409
EP - 423
BT - Web and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Proceedings
A2 - Wang, Xin
A2 - Zhang, Rui
A2 - Lee, Young-Koo
A2 - Sun, Le
A2 - Moon, Yang-Sae
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020
Y2 - 18 September 2020 through 20 September 2020
ER -