Accurate and Explainable Recommendation via Hierarchical Attention Network Oriented Towards Crowd Intelligence

doi:10.1016/j.knosys.2020.106687

	Accurate and Explainable Recommendation via Hierarchical Attention Network Oriented Towards Crowd Intelligence
	Yang, Chao 1; Zhou, Weixin 1; Wang, Zhiyu 1; Jiang, Bin 1; Li, Dongsheng 2; Shen, Huawei 3
	2021-02-15
发表期刊	KNOWLEDGE-BASED SYSTEMS
ISSN	0950-7051
卷号	213 页码:13
摘要	Review-based recommendation algorithms can alleviate the data sparsity issue in collaborative filtering by combining user ratings and reviews in model learning. However, most existing methods simplify the feature extraction process from reviews by assuming that different granularities of information (e.g., word, review, and feature) are equally important, which cannot optimally leverage the most important information and thus achieves suboptimal recommendation accuracy. Besides, many existing works directly regard text features as users or items representations, which may not be enough to make precise representations due to the large amount of redundant information in reviews. To tackle the two problems mentioned above, we propose a deep learning-based method named Hierarchical Attention Network Oriented Towards Crowd Intelligence (HANCI). First, HANCI replaces the commonly-used topic models or CNN text processor with an RNN text processor in review feature extraction, which can fully exploit the advantages of the sequential dependencies of reviews by using the whole hidden layers of the bidirectional LSTM as outputs. Second, HANCI weighs the importance of features guided by crowd intelligence to more accurately represent each user on each item, and vice versa. Third, HANCI utilizes a hierarchical attention network based on multi-level review text analysis to extract more precise user preferences and item latent features, so that HANCI can explore the importance of words, the usefulness of reviews and the importance of features to achieve more accurate recommendation. Extensive experiments on three public datasets show that HANCI outperforms the state-of-the-art review-based recommendation algorithms in accuracy and meanwhile provides insightful explanations. (C) 2020 Elsevier B.V. All rights reserved.
关键词	Crowd intelligence Explainable recommendation Hierarchical attention Review representation Recommender system
DOI	10.1016/j.knosys.2020.106687
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61702176] ; National Natural Science Foundation of China[62072169] ; CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences[CASNDST202002]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000614642900006
出版者	ELSEVIER
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16264
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yang, Chao
作者单位	1.Hunan Univ, Coll Comp Sci & Elect Engn, Lushan Rd S, Changsha, Peoples R China 2.Microsoft Res Asia, 77 Hongcao Rd, Shanghai, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Chao,Zhou, Weixin,Wang, Zhiyu,et al. Accurate and Explainable Recommendation via Hierarchical Attention Network Oriented Towards Crowd Intelligence[J]. KNOWLEDGE-BASED SYSTEMS,2021,213:13.
APA	Yang, Chao,Zhou, Weixin,Wang, Zhiyu,Jiang, Bin,Li, Dongsheng,&Shen, Huawei.(2021).Accurate and Explainable Recommendation via Hierarchical Attention Network Oriented Towards Crowd Intelligence.KNOWLEDGE-BASED SYSTEMS,213,13.
MLA	Yang, Chao,et al."Accurate and Explainable Recommendation via Hierarchical Attention Network Oriented Towards Crowd Intelligence".KNOWLEDGE-BASED SYSTEMS 213(2021):13.