Review Rating Prediction on Location-Based Social Networks Using Text, Social Links, and Geolocations

Yuehua WANG  Zhinong ZHONG  Anran YANG  Ning JING  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.9   pp.2298-2306
Publication Date: 2018/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7180
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
review rating prediction,  location-based social networks,  multi-class classification,  ensemble algorithm,  

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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.