TY - GEN
T1 - Social personalized ranking embedding for next POI recommendation
AU - Long, Yan
AU - Zhao, Pengpeng
AU - Sheng, Victor S.
AU - Liu, Guanfeng
AU - Xu, Jiajie
AU - Wu, Jian
AU - Cui, Zhiming
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - As the increasing popularity of the applications of location-based services, points-of-interest (POI) recommendation has become a great value part to help users explore their surrounding living environment and improve the quality of life. Recently, some researchers proposed next POI recommendation, which not only exploiting the users personal interests but also considers the sequential information of users check-ins. There are some next POI recommendation models exploit Metric Embedding method to improve recommendation performance and efficiency. However, these approaches not consider social relations in next POI recommendation, which is challenging due to social relations are noisy and sparse. To this end, in this paper, we proposed a Social Personalized Ranking Embedding (SPRE) model, which integrates user personalization and social relations into consideration, to learn the social relations by social embedding for next POI recommendation. Our experiments on a real-world large-scale dataset (Foursquare) results show that our model outperforms the state-of-the-art next POI recommendation methods.
AB - As the increasing popularity of the applications of location-based services, points-of-interest (POI) recommendation has become a great value part to help users explore their surrounding living environment and improve the quality of life. Recently, some researchers proposed next POI recommendation, which not only exploiting the users personal interests but also considers the sequential information of users check-ins. There are some next POI recommendation models exploit Metric Embedding method to improve recommendation performance and efficiency. However, these approaches not consider social relations in next POI recommendation, which is challenging due to social relations are noisy and sparse. To this end, in this paper, we proposed a Social Personalized Ranking Embedding (SPRE) model, which integrates user personalization and social relations into consideration, to learn the social relations by social embedding for next POI recommendation. Our experiments on a real-world large-scale dataset (Foursquare) results show that our model outperforms the state-of-the-art next POI recommendation methods.
KW - Metric embedding
KW - Next POI recommendation
KW - Social relations influence
UR - http://www.scopus.com/inward/record.url?scp=85031405965&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68783-4_7
DO - 10.1007/978-3-319-68783-4_7
M3 - Conference contribution
AN - SCOPUS:85031405965
SN - 9783319687827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 91
EP - 105
BT - Web Information Systems Engineering – WISE 2017 - 18th International Conference, Proceedings
A2 - Chen, Lu
A2 - Bouguettaya, Athman
A2 - Klimenko, Andrey
A2 - Dzerzhinskiy, Fedor
A2 - Klimenko, Stanislav V.
A2 - Zhang, Xiangliang
A2 - Li, Qing
A2 - Gao, Yunjun
A2 - Jia, Weijia
PB - Springer-Verlag
Y2 - 7 October 2017 through 11 October 2017
ER -