3 years ago

NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks

Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, Jingyuan Yang, Hui Xiong

Publication date: Available online 8 January 2019

Source: Neural Networks

Author(s): Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, Jingyuan Yang, Hui Xiong


Traditional recommender systems rely on user profiling based on either user ratings or reviews through bi-sentimental analysis. However, in real-world scenarios, there are two common phenomena: (1) users only provide ratings for items but without detailed review comments. As a result, the historical transaction data available for recommender systems are usually unbalanced and sparse; (2) in many cases, users’ opinions can be better grasped in their reviews than ratings. For the reason that there is always a bias between ratings and reviews, it is really important that users’ ratings and reviews should be mutually reinforced to grasp the users’ true opinions. To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation. Specifically, we exploit a two-step training neural networks, which utilize both reviews and ratings to grasp users’ true opinions in unbalanced data. Moreover, we propose a Sentiment Classification scoring (SC) method, which employs dual attention vectors to predict the users’ sentiment scores of their reviews rather than using bi-sentiment analysis. Next, a combination function is designed to use the results of SC and user-item rating matrix to catch the opinion bias. It can filter the reviews and users, and build an enhanced user-item matrix. Finally, a Multilayer perceptron based Matrix Factorization (MMF) method is proposed to make recommendations with the enhanced user-item matrix. Extensive experiments on several real-world datasets (Yelp, Amazon, Taobao and Jingdong) demonstrate that (1) our approach can achieve a superior performance over state-of-the-art baselines; (2) our approach is able to tackle unbalanced data and achieve stable performances. 

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