Music Recommender System using Autorec Method for Implicit Feedback
DOI:
https://doi.org/10.30865/mib.v7i2.5653Keywords:
Recommender System, Autoencoder, Deep Learning, Music Recommender System, AutorecAbstract
As the number of music and users in music streaming services increases, the role of music recommender systems is getting important to make it easier for users to find music that matches their tastes. The collaborative filtering paradigm is the most commonly used paradigm in developing recommender systems. Many studies have proven that deep learning is able to improve the performance of matrix factorization. One such method in deep learning that has been adapted for use in Recommender Systems is Autorec, which is a variation of the Autoencoder technique. Autorec shows that it performs better than the baseline matrix factorization using Movielens and Netflix datasets. Therefore, in this study we propose the use of Autorec to develop a recommender system for music. The experimental results show that Autorec performs better than Singular Value Decomposition (SVD), with an RMSE difference of 0.7.References
M. Schedl, H. Zamani, C. W. Chen, Y. Deldjoo, and M. Elahi, “Current challenges and visions in music recommender systems research,†Int J Multimed Inf Retr, vol. 7, no. 2, pp. 95–116, Jun. 2018, doi: 10.1007/S13735-018-0154-2/FIGURES/1.
Z. K. A. Baizal, D. H. Widyantoro, and N. U. Maulidevi, “Design of knowledge for conversational recommender system based on product functional requirements,†Proceedings of 2016 International Conference on Data and Software Engineering, ICoDSE 2016, May 2017, doi: 10.1109/ICODSE.2016.7936151.
Z. K. A. Baizal, D. Tarwidi, Adiwijaya, and B. Wijaya, “Tourism Destination Recommendation Using Ontology-based Conversational Recommender System,†International Journal of Computing and Digital Systems, vol. 10, no. 1, pp. 829–838, 2021, doi: 10.12785/IJCDS/100176.
Q. Guo et al., “A Survey on Knowledge Graph-Based Recommender Systems,†IEEE Trans Knowl Data Eng, vol. 34, no. 8, pp. 3549–3568, Aug. 2022, doi: 10.1109/TKDE.2020.3028705.
S. Schönbrodt and M. Frank, “Data Science and Machine Learning in mathematics education: Highschool students working on the Netflix Prize,†vol. TWG05, no. 24, Feb. 2022, Accessed: Feb. 10, 2023. [Online]. Available: https://hal.science/hal-03754716
A. Singhal, P. Sinha, and R. Pant, “Use of deep learning in modern recommendation system: A summary of recent works,†arXiv preprint arXiv:1712.07525, 2017.
S. Khan, “Ethem Alpaydin. Introduction to Machine Learning (Adaptive Computation and Machine Learning Series).,†Nat Lang Eng, 2020.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,†Nature, vol. 521, no. 7553, pp. 436–444, 2015.
G. Zhang, Y. Liu, and X. Jin, “A survey of autoencoder-based recommender systems,†Front Comput Sci, vol. 14, no. 2, pp. 430–450, 2020.
S. Sedhain, A. K. Menon, S. Sanner, and L. Xie, “Autorec: Autoencoders meet collaborative filtering,†in Proceedings of the 24th international conference on World Wide Web, 2015, pp. 111–112.
O. Kuchaiev and B. Ginsburg, “Training deep autoencoders for collaborative filtering,†arXiv preprint arXiv:1708.01715, 2017.
D. Liang, R. G. Krishnan, M. D. Hoffman, and T. Jebara, “Variational autoencoders for collaborative filtering,†in Proceedings of the 2018 world wide web conference, 2018, pp. 689–698.
Y. Wu, C. DuBois, A. X. Zheng, and M. Ester, “Collaborative denoising auto-encoders for top-n recommender systems,†in Proceedings of the ninth ACM international conference on web search and data mining, 2016, pp. 153–162.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.
X. Peng, Y. Li, X. Wei, J. Luo, and Y. L. Murphey, “Traffic sign recognition with transfer learning,†in 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017, pp. 1–7.
A. Dehghan, E. G. Ortiz, R. Villegas, and M. Shah, “Who do i look like? determining parent-offspring resemblance via gated autoencoders,†in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1757–1764.
X. Lu, Y. Tsao, S. Matsuda, and C. Hori, “Speech enhancement based on deep denoising autoencoder.,†in Interspeech, 2013, vol. 2013, pp. 436–440.
K. Wang, M. G. Forbes, B. Gopaluni, J. Chen, and Z. Song, “Systematic Development of a New Variational Autoencoder Model Based on Uncertain Data for Monitoring Nonlinear Processes,†IEEE Access, vol. 7, pp. 22554–22565, 2019, doi: 10.1109/ACCESS.2019.2894764.
D. Bank, N. Koenigstein, and R. Giryes, “Autoencoders,†arXiv preprint arXiv:2003.05991, 2020.
N. Seaver, “Captivating algorithms: Recommender systems as traps,†https://doi.org/10.1177/1359183518820366, vol. 24, no. 4, pp. 421–436, Dec. 2018, doi: 10.1177/1359183518820366.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
