For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
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
Publication Date: 2018/09/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
review rating prediction, location-based social networks, multi-class classification, ensemble algorithm,
Full Text: PDF(2.9MB)
>>Buy this Article
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.