Understanding Rating Behaviour and Predicting Ratings by Identifying Representative Users

TL;DR

This paper explores how to understand user rating behaviors and predict product ratings by identifying representative users along different rating dimensions, especially in restaurant reviews, with fewer ratings and no review texts.

Contribution

The study introduces a method to identify representative users for each rating dimension and demonstrates improved rating prediction accuracy with limited data.

Findings

Outperforms conventional methods by 16-27% in RMSE

Effective with few user ratings and no review texts

Identifies key user groups influencing ratings across dimensions

Abstract

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.