IEICE TRANSACTIONS on Information
Review Rating Prediction on Location-Based Social Networks Using Text, Social Links, and Geolocations
Summary :
Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.9 pp.2298-2306
- Publication Date
- 2018/09/01
- Publicized
- 2018/06/01
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7180
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Yuehua WANG
National University of Defense Technology
Zhinong ZHONG
National University of Defense Technology
Anran YANG
National University of Defense Technology
Ning JING
National University of Defense Technology
Keyword
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Yuehua WANG, Zhinong ZHONG, Anran YANG, Ning JING, "Review Rating Prediction on Location-Based Social Networks Using Text, Social Links, and Geolocations" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 9, pp. 2298-2306, September 2018, doi: 10.1587/transinf.2017EDP7180.
Abstract: Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7180/_p
Copy
@ARTICLE{e101-d_9_2298,
author={Yuehua WANG, Zhinong ZHONG, Anran YANG, Ning JING, },
journal={IEICE TRANSACTIONS on Information},
title={Review Rating Prediction on Location-Based Social Networks Using Text, Social Links, and Geolocations},
year={2018},
volume={E101-D},
number={9},
pages={2298-2306},
abstract={Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.},
keywords={},
doi={10.1587/transinf.2017EDP7180},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Review Rating Prediction on Location-Based Social Networks Using Text, Social Links, and Geolocations
T2 - IEICE TRANSACTIONS on Information
SP - 2298
EP - 2306
AU - Yuehua WANG
AU - Zhinong ZHONG
AU - Anran YANG
AU - Ning JING
PY - 2018
DO - 10.1587/transinf.2017EDP7180
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2018
AB - Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.
ER -
English
