NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks
文献类型：期刊论文
| 作者 | Yang, Jingyuan2; Xu, Yuanbo5; Yang, Yongjian5; Han, Jiayu5; Wang, En5; Zhuang, Fuzhen3,4; Xiong, Hui1 |
| 刊名 | NEURAL NETWORKS
 |
| 出版日期 | 2019-03-01 |
| 卷号 | 111页码:77-88 |
| 关键词 | Opinion bias Recommender systems Convolutional neural network Dual attention vectors |
| ISSN号 | 0893-6080 |
| DOI | 10.1016/j.neunet.2018.12.011 |
| 英文摘要 | 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 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. (C) 2018 Elsevier Ltd. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[61772230] ; National Natural Science Foundation of China[61773361] ; National Natural Science Foundation of China[U1836206] ; China Postdoctoral Science Foundation[2017M611322] ; China Postdoctoral Science Foundation[2018T110247] ; Natural Science Foundation of China for Young Scholars[61702215] |
| WOS研究方向 | Computer Science ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000458132700006 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/3432]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wang, En |
| 作者单位 | 1.Rutgers State Univ, New Brunswick, NJ USA 2.George Mason Univ, Fairfax, VA 22030 USA 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 5.Jilin Univ, Changchun, Jilin, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Jingyuan,Xu, Yuanbo,Yang, Yongjian,et al. NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks[J]. NEURAL NETWORKS,2019,111:77-88. |
| APA | Yang, Jingyuan.,Xu, Yuanbo.,Yang, Yongjian.,Han, Jiayu.,Wang, En.,...&Xiong, Hui.(2019).NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks.NEURAL NETWORKS,111,77-88. |
| MLA | Yang, Jingyuan,et al."NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks".NEURAL NETWORKS 111(2019):77-88. |
入库方式： OAI收割
来源：计算技术研究所
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