中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
TNAM: A tag-aware neural attention model for Top-N recommendation

文献类型：期刊论文

作者Huang, Ruoran1,2; Wang, Nian1,2; Han, Chuanqi1,2; Yu, Fang1,2; Cui, Li1
刊名NEUROCOMPUTING
出版日期2020-04-14
卷号385页码:1-12
关键词Recommender systems Tag information Deep learning Attention networks
ISSN号0925-2312
DOI10.1016/j.neucom.2019.11.095
英文摘要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.
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
语种英语
WOS记录号WOS:000517884400001
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/14475]  
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。