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Improving POI Recommendation via Dynamic Tensor Completion
Joint Authors
Zhao, Xiang
Liao, Jinzhi
Tang, Jiuyang
Shang, Haichuan
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-13
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
POI recommendation finds significant importance in various real-life applications, especially when meeting with location-based services, e.g., check-ins social networks.
In this paper, we propose to solve POI recommendation through a novel model of dynamic tensor, which is among the first triumphs of its kind.
In order to carry out timely recommendation, we predict POI by utilizing a completion algorithm based on fast low-rank tensor.
Particularly, the dynamic tensor structure is complemented by the fast low-rank tensor completion algorithm so as to achieve prediction with better performance, where the parameter optimization is achieved by a pigeon-inspired heuristic algorithm.
In short, our POI recommendation via the dynamic tensor method can take advantage of the intrinsic characteristics of check-ins data due to the multimode features such as current categories, subsequent categories, and temporal information as well as seasons variations are all integrated into the model.
Extensive experiment results not only validate the superiority of our proposed method but also imply the application prospect in large-scale and real-time POI recommendation environment.
American Psychological Association (APA)
Liao, Jinzhi& Tang, Jiuyang& Zhao, Xiang& Shang, Haichuan. 2018. Improving POI Recommendation via Dynamic Tensor Completion. Scientific Programming،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1214684
Modern Language Association (MLA)
Liao, Jinzhi…[et al.]. Improving POI Recommendation via Dynamic Tensor Completion. Scientific Programming No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1214684
American Medical Association (AMA)
Liao, Jinzhi& Tang, Jiuyang& Zhao, Xiang& Shang, Haichuan. Improving POI Recommendation via Dynamic Tensor Completion. Scientific Programming. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1214684
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214684