Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music Recommendations
Lin, Qika1; Niu, Yaoqiang2; Zhu, Yifan1; Lu, Hao1,3; Mushonga, Keith Zvikomborero1; Niu, Zhendong1,4
发表期刊IEEE ACCESS
ISSN2169-3536
2018
卷号6页码:58990-59000
通讯作者Niu, Zhendong(zniu@bit.edu.cn)
摘要The current existing data in online music service platforms are heterogeneous, extensive, and disorganized. Finding an effective method to use these data in recommending appropriate music to users during a short-term session is a significant challenge. Another serious problem is that most of the data, in reality, obey the long-tailed distribution, which consequently leads to traditional music recommendation systems recommending a lot of popular music that users do not like on a specific occasion. To solve these problems, we propose a heterogeneous knowledge-based attentive neural network model for short-term music recommendations. First, we collect three types of data for modeling entities in user-music interaction network, i.e., graphic, textual, and visual data, and then embed them into high-dimensional spaces using the TransR, distributed memory version of paragraph vector, and variational autoencoder methods, respectively. The concatenation of these embedding results is an abstract representation of the entity. Based on this, a recurrent neural network with an attention mechanism is built, which is capable of obtaining users' preferences in the current session and consequently making recommendations. The experimental results show that our proposed approach outperforms the current state-of-the-art short-term music recommendation systems on one real-world dataset. In addition, it can also recommend more relatively unpopular songs compared with classic models.
关键词Heterogeneous knowledge data embedding entity representation attentive neural networks short-term music recommendation
DOI10.1109/ACCESS.2018.2874959
关键词[WOS]CHALLENGES ; SYSTEM
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[61370137] ; Ministry of Education-China Mobile Research Foundation Project[2016/2-7]
项目资助者National Natural Science Foundation of China ; Ministry of Education-China Mobile Research Foundation Project
WOS研究方向Computer Science ; Engineering ; Telecommunications
WOS类目Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号WOS:000449548300001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/22609
专题复杂系统管理与控制国家重点实验室_平行控制
通讯作者Niu, Zhendong
作者单位1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 10081, Peoples R China
2.Lanzhou Jiaotong Univ, Sch Comp Technol, Lanzhou 730000, Gansu, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA 15260 USA
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GB/T 7714
Lin, Qika,Niu, Yaoqiang,Zhu, Yifan,et al. Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music Recommendations[J]. IEEE ACCESS,2018,6:58990-59000.
APA Lin, Qika,Niu, Yaoqiang,Zhu, Yifan,Lu, Hao,Mushonga, Keith Zvikomborero,&Niu, Zhendong.(2018).Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music Recommendations.IEEE ACCESS,6,58990-59000.
MLA Lin, Qika,et al."Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music Recommendations".IEEE ACCESS 6(2018):58990-59000.
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