Are Ratings Always Reliable? Discover Users True Feelings with Textual Reviews
In e-commerce systems,users ratings play an important role in many scenarios such as reputation and trust mechanisms and recommender systems.A general assumption in these techniques is that users ratings represent their true feelings.Although it has long been adopted in previous work,this assumption is not necessarily true.In this paper,we first present an in-depth study of the inconsistency between users ratings and their reviews.Then we propose an approach to mine users “true ratings which better represent their real feelings,from textual reviews based on Gated Recurrent Unit(GRU)and hierarchical attention techniques.One major contribution is that we are about the first,to the best of our knowledge,to investigate this new problem of discovering users true ratings,and to provide direct solutions to revise ratings that are insincere and inconsistent.Comparative experiments on a real e-commerce dataset have been conducted,which show that the “true ratings learned by the proposed model is significantly better than the original ones in terms of consistency with the reviews in three sets of crowdsourcing-based evaluations.Furthermore,leveraging different state-of-art recommendation approaches based on the learned “true ratings,more effective results have been achieved at all times in rating prediction task.
Rating revision Review to score Deep learning for recommendation
Bin Hao Min Zhang Yunzhi Tan Yiqun Liu Shaoping Ma
Department of Computer Science and Technology,Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China
国际会议
2018自然语言处理与中文计算国际会议(NLPCC2018)
呼和浩特
英文
442-453
2018-08-26（万方平台首次上网日期，不代表论文的发表时间）