Jointly modeling heterogeneous temporal properties in location recommendation

Hosseini, Saeid, Yin, Hongzhi, Zhang, Meihui, Zhou, Xiaofang and Sadiq, Shazia Wasim (2017). Jointly modeling heterogeneous temporal properties in location recommendation. In: Selçuk Candan, Lei Chen, Torben Bach Pedersen, Lijun Chang and Wen Hua, 22nd Internation Conference, DASFAA 2017. 22nd Internation Conference, DASFAA 2017, Suzhou, China, (490-506). 27 - 30 March 2017. doi:10.1007/978-3-319-55753-3_31

Author Hosseini, Saeid
Yin, Hongzhi
Zhang, Meihui
Zhou, Xiaofang
Sadiq, Shazia Wasim
Title of paper Jointly modeling heterogeneous temporal properties in location recommendation
Conference name 22nd Internation Conference, DASFAA 2017
Conference location Suzhou, China
Conference dates 27 - 30 March 2017
Proceedings title 22nd Internation Conference, DASFAA 2017   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Cham, Switzerland
Publisher Springer
Publication Year 2017
Year available 2017
Sub-type Fully published paper
DOI 10.1007/978-3-319-55753-3_31
Open Access Status Not yet assessed
ISBN 9783319557526
ISSN 0302-9743
Editor Selçuk Candan
Lei Chen
Torben Bach Pedersen
Lijun Chang
Wen Hua
Volume 10177
Start page 490
End page 506
Total pages 17
Language eng
Abstract/Summary Point-Of-Interest (POI) recommendation systems suggest interesting locations to users based on their previous check-ins via location-based social networks (LBSNs). Individuals visiting a location are partially affected by many factors including social links, travel distance and the time. A growing line of research has been devoted to taking advantage of various effects to improve existing location recommendation methods. However, the temporal influence owns numerous dimensions which deserve to be explored more in depth. The subset property comprises a set of homogeneous slots such as an hour of the day, the day of the week, week of the month, month of the year, and so on. In addition, time has other attributes such as the recency which signifies the newly visited locations versus others. In this paper, we further study the role of time factor in recommendation models. Accordingly, we define a new problem to jointly model a pair of heterogeneous time-related effects (recency and the subset feature) in location recommendation.
Subjects 2614 Theoretical Computer Science
1700 Computer Science
Keyword Framework
Q-Index Code E1
Q-Index Status Provisional Code
Grant ID SRG ISTD 2014 084
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
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Created: Thu, 14 Sep 2017, 23:07:53 EST by Hongzhi Yin on behalf of School of Information Technol and Elec Engineering