Paper Info
Title
Time-aware metric embedding with asymmetric projection for successive POI recommendation
Abstract
Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall.
Year
DOI
Venue
2019
10.1007/s11280-018-0596-8
World Wide Web
Keywords
Field
DocType
Successive POI recommendation, Metric embedding, Asymmetric projection, Temporal influence
Data mining,Social network,Embedding,Computer science,Precision and recall,Dynamic factor,Exploit,Artificial intelligence,Euclidean geometry,Machine learning
Journal
Volume
Issue
ISSN
22
SP5
1573-1413
Citations 
PageRank 
References 
5
0.44
32
Authors
7
Name
Order
Citations
PageRank
Haochao Ying17310.03
Jian Wu293395.62
Guandong Xu364075.03
Yanchi Liu469845.70
Tingting Liang551.11
Xiao Zhang695.54
Hui Xiong74958290.62