  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.
