Point-of-interest recommendation using temporal orientations of users and locations

Hosseini, Saeid and Li, Lei Thor (2016). Point-of-interest recommendation using temporal orientations of users and locations. In: Shamkant B. Navathe, Weili Wu, Shashi Shekhar, Xiaoyong Du, X. Sean Wang and Hui Xiong, Database Systems for Advanced Applications - 21st International Conference, DASFAA 2016, Proceedings. 21st International Conference on Database Systems for Advanced Applications, DASFAA 2016, Dallas, United States, (330-347). 16-19 April 2016. doi:10.1007/978-3-319-32025-0_21


Author Hosseini, Saeid
Li, Lei Thor
Title of paper Point-of-interest recommendation using temporal orientations of users and locations
Conference name 21st International Conference on Database Systems for Advanced Applications, DASFAA 2016
Conference location Dallas, United States
Conference dates 16-19 April 2016
Proceedings title Database Systems for Advanced Applications - 21st International Conference, DASFAA 2016, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Publisher Springer Verlag
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1007/978-3-319-32025-0_21
Open Access Status Not Open Access
ISBN 978331932024-3
9783319320250
ISSN 1611-3349
0302-9743
Editor Shamkant B. Navathe
Weili Wu
Shashi Shekhar
Xiaoyong Du
X. Sean Wang
Hui Xiong
Volume 9642
Start page 330
End page 347
Total pages 18
Collection year 2017
Language eng
Abstract/Summary Location Based Social Networks (LBSN) promotes communications among subscribers. Utilizing online check-in data supplied via LBSN, Point-Of-Interest (POI) recommendation systems propose unvisited relevant venues to the users. Various techniques have been designed for POI recommendation systems. However, diverse temporal information has not been studied adequately. From temporal perspective, as visited locations during weekday and weekend are marginally different, we choose weekly intervals to improve effectiveness of POI recommenders. However, our method is also applicable to other similar periodic intervals. People usually visit tourist and leisure spots during weekends and work related places during weekdays. Similarly, some users perform check-ins mostly during weekend, while others prefer weekday predominantly. In this paper, we define a new problem to perform recommendation, based on temporal weekly alignments of users and POIs. We argue that locations with higher popularity should be more influential. Therefore, In order to solve the problem, we develop a probabilistic model which initially detects a user’s temporal orientation based on visibility weights of POIs visited by her. As a step further, we develop a recommender framework that proposes proper POIs to the user according to her temporal weekly preference. Moreover, we take succeeding POI pairs visited by the same user into consideration to develop a more efficient temporal model to handle geographical information. Extensive experimental results on two large-scale LBSN datasets verify that our method outperforms current state-of-the-art recommendation techniques.
Keyword Location-based social networks
Point-of-interest recommendation
Temporal influence
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Tue, 26 Apr 2016, 02:16:55 EST by System User on behalf of Learning and Research Services (UQ Library)