Understanding Rating Behaviour and Predicting Ratings by Identifying Representative Users

Online user reviews describing various products and services are now abundant on the web. While the information conveyed through review texts and ratings is easily comprehensible, there is a wealth of hidden information in them that is not immediately obvious. In this study, we unlock this hidden value behind user reviews to understand the various dimensions along which users rate products. We learn a set of users that represent each of these dimensions and use their ratings to predict product ratings. Specifically, we work with restaurant reviews to identify users whose ratings are influenced by dimensions like 'Service', 'Atmosphere' etc. in order to predict restaurant ratings and understand the variation in rating behaviour across different cuisines. While previous approaches to obtaining product ratings require either a large number of user ratings or a few review texts, we show that it is possible to predict ratings with few user ratings and no review text. Our experiments show that our approach outperforms other conventional methods by 16-27% in terms of RMSE.