Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music Recommendations
文献类型：期刊论文
| 作者 | Lin, Qika1; Niu, Yaoqiang2; Zhu, Yifan1; Lu, Hao1,3 ; Mushonga, Keith Zvikomborero1; Niu, Zhendong1,4
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| 刊名 | IEEE ACCESS
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| 出版日期 | 2018 |
| 卷号 | 6页码:58990-59000 |
| 关键词 | Heterogeneous knowledge data embedding entity representation attentive neural networks short-term music recommendation |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2018.2874959 |
| 通讯作者 | Niu, Zhendong(zniu@bit.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | CHALLENGES ; SYSTEM |
| 资助项目 | National Natural Science Foundation of China[61370137] ; Ministry of Education-China Mobile Research Foundation Project[2016/2-7] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000449548300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Ministry of Education-China Mobile Research Foundation Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/22608]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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