Music recommender using deep embedding-based features and behavior-based reinforcement learning-Reference-Cited by-同舟云学术

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Music recommender using deep embedding-based features and behavior-based reinforcement learning

Published:2019-12-27 Issue:26-27 Volume:80 Page:34037-34064
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Chang Jia-Wei,Chiou Ching-Yi,Liao Jia-Yi,Hung Ying-Kai,Huang Chien-Che,Lin Kuan-Cheng,Pu Ying-Hung

Funder

Ministry of Science and Technology, Taiwan

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-019-08356-9.pdf

Reference32 articles.

1. Hurley N, Zhang M (2011) Novelty and diversity in top-n recommendation – analysis and evaluation. ACM Trans Internet Technol (TOIT) 10(4):14

2. Cheng Z, Shen J, Nie L, Chua TS, Kankanhalli M (2017) Exploring user-specific information in music retrieval. In: Proceedings of the 40th international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, pp 655–664

3. Wang X, Wang Y (2014) Improving content-based and hybrid music recommendation using deep learning. In: Proceedings of the 22nd ACM international conference on Multimedia. ACM, pp 627–636

4. Logan B (2004) Music recommendation from song sets. In: ISMIR, pp 425–428

5. Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. In: Recommender systems handbook. Springer, Boston, pp 1–35

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