TNAM: A tag-aware neural attention model for Top-N recommendation

doi:10.1016/j.neucom.2019.11.095

	TNAM: A tag-aware neural attention model for Top-N recommendation
	Huang, Ruoran 1,2; Wang, Nian 1,2; Han, Chuanqi 1,2; Yu, Fang 1,2; Cui, Li 1
	2020-04-14
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	385 页码:1-12
摘要	Recent work shows that incorporating tag information to recommender systems is promising for improving the recommendation accuracy in social systems. However, existing approaches suffer from less reasonable assignment of tag weights when constructing the user profiles and item characteristics in real-world scenarios, resulting in decreased accuracy in making recommendations. The above issue is specifically summarized into two aspects: 1) the weight of a target item is mainly determined by number of one certain type of tags, and 2) users place equal focus on the same tag for different items. To tackle these problems, we propose a novel model named TNAM, a Tag-aware Neural Attention Model, which accurately captures users' special attention to tags of items. In the proposed model, we design a tag-based neural attention network by extracting potential tag information to overcome the difficulty of assigning tag weights for personalized users. We combine user-item interactions with tag information to map sparse data to dense vectors in higher-order space. In this way, TNAM acquires more interrelations between users and items to make recommendations more accurate. Extensive experiments of our model on three publicly implicit feedback datasets reveal significant improvements on the metrics of HR and NDCG in Top-N recommendation tasks over several state-of-the-art approaches. (C) 2019 Published by Elsevier B.V.
关键词	Recommender systems Tag information Deep learning Attention networks
DOI	10.1016/j.neucom.2019.11.095
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[61672498] ; National Key Research and Development Program of China[2016YFC0302300]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000517884400001
出版者	ELSEVIER
引用统计	被引频次：20[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/2XEOYT63/14476
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cui, Li
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Huang, Ruoran,Wang, Nian,Han, Chuanqi,et al. TNAM: A tag-aware neural attention model for Top-N recommendation[J]. NEUROCOMPUTING,2020,385:1-12.
APA	Huang, Ruoran,Wang, Nian,Han, Chuanqi,Yu, Fang,&Cui, Li.(2020).TNAM: A tag-aware neural attention model for Top-N recommendation.NEUROCOMPUTING,385,1-12.
MLA	Huang, Ruoran,et al."TNAM: A tag-aware neural attention model for Top-N recommendation".NEUROCOMPUTING 385(2020):1-12.