MMusic: a hierarchical multi-information fusion method for deep music recommendation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10844-023-00786-0.pdf
Reference34 articles.
1. Alharbi, N., & Caragea, D. (2021). Cross-domain attentive sequential recommendations based on general and current user preferences (cd-asr). In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 48–55). Association for Computing Machinery. https://doi.org/10.1145/3486622.3493949.
2. Ben Sassi, I., & Ben Yahia, S. (2021). How does context influence music preferences: a user-based study of the effects of contextual information on users’ preferred music. Multimedia Systems, 27, 143–160. https://doi.org/10.1007/s00530-020-00717-x
3. Cami, B. R., Hassanpour, H., & Mashayekhi, H. (2019). User preferences modeling using dirichlet process mixture model for a content-based recommender system. Knowledge-Based Systems, 163, 644–655. https://doi.org/10.1016/j.knosys.2018.09.028
4. Cao, Y., & Liu, P. (2022). Personalized music hybrid recommendation algorithms fusing gene features. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/9209022.
5. de Assunção, W. G., & Zaina, L. A. M. (2022). Evaluating user experience in music discovery on deezer and spotify. In Proceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems (pp. 1–11). Association for Computing Machinery. https://doi.org/10.1145/3554364.3560901.
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