KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Automatic Tag Classification from Sound Data for Graph-Based Music Recommendation

그래프 기반 음악 추천을 위한 소리 데이터를 통한 태그 자동 분류

  • 김태진 (숭실대학교 융합소프트웨어학과) ;
  • 김희찬 (숭실대학교 융합소프트웨어학과) ;
  • 이수원 (숭실대학교 소프트웨어학부)
  • Received : 2021.02.08
  • Accepted : 2021.06.18
  • Published : 2021.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

With the steady growth of the content industry, the need for research that automatically recommending content suitable for individual tastes is increasing. In order to improve the accuracy of automatic content recommendation, it is needed to fuse existing recommendation techniques using users' preference history for contents along with recommendation techniques using content metadata or features extracted from the content itself. In this work, we propose a new graph-based music recommendation method which learns an LSTM-based classification model to automatically extract appropriate tagging words from sound data and apply the extracted tagging words together with the users' preferred music lists and music metadata to graph-based music recommendation. Experimental results show that the proposed method outperforms existing recommendation methods in terms of the recommendation accuracy.

콘텐츠 산업의 꾸준한 성장에 따라 수많은 콘텐츠 중에서 개인의 취향에 적합한 콘텐츠를 자동으로 추천하는 연구의 필요성이 증가하고 있다. 콘텐츠 자동 추천의 정확도를 향상시키기 위해서는 콘텐츠에 대한 사용자의 선호 이력을 바탕으로 하는 기존 추천 기법과 더불어 콘텐츠의 메타데이터 및 콘텐츠 자체에서 추출할 수 있는 특징을 융합한 추천 기법이 필요하다. 본 연구에서는 음악의 소리 데이터로부터 태그 정보를 분류하는 LSTM 기반의 모델을 학습하고 분류된 태그 정보를 음악의 메타 데이터로 추가하여, 그래프 임베딩 시 콘텐츠의 특징까지 고려할 수 있는 KPRN 기반의 새로운 콘텐츠 추천 방법을 제안한다. 카카오 아레나 데이터 기반 실험 결과, 본 연구의 제안 방법은 기존의 임베딩 기반 추천 방법보다 우수한 추천 정확도를 보였다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터 지원사업의 연구결과로 수행되었음(IITP-2021-2018-0-01419).

References

  1. U. Shardanand and P. Maes, "Social information filtering: Algorithms for automating 'word of mouth'," In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp.210-217, 1995.
  2. D. Sanchez-Moreno, A. B. G. Gonzalez, M. D. M. Vicente, V. F. L. Batista, and M. N. M. Garcia, "A collaborative filtering method for music recommendation using playing coefficients for artists and users," Expert Systems with Applications, Vol.66, pp.234-244, 2016. https://doi.org/10.1016/j.eswa.2016.09.019
  3. E. Shakirova, "Collaborative filtering for music recommender system," In Proceedings of the IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, pp.548-550, 2017.
  4. D. Wang, X. Guandong, and D. Shuiguang, "Music recommendation via heterogeneous information graph embedding," In Proceedings of the International Joint Conference on Neural Networks, pp.596-603, 2017.
  5. Z. Wang, J. Zhang, J. Feng, and Z. Chen, "Knowledge graph embedding by translating on hyperplanes," In Proceedings of the AAAI Conference on Artificial Intelligence, pp.1112-1119, 2014.
  6. X. Wang, D. Wang, C. Xu, X. He, Y. Cao, and T. S. Chua, "Explainable reasoning over knowledge graphs for recommendation," In Proceedings of the AAAI Conference on Artificial Intelligence, pp.5329-5336, 2019.
  7. K. Choi, G. Fazekas, and M. Sandler, "Automatic tagging using deep convolutional neural networks," arXiv preprint arXiv: 1606.00298, 2016.
  8. J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp.43-52, 1998.
  9. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," In Proceedings of the International Conference on World Wide Web, pp.285-295, 2001.
  10. Y. Koren, "Factorization meets the neighborhood: A multifaceted collaborative filtering model," In Proceedings of the ACM SIGKDD IInternational Conference on Knowledge Discovery and Data Mining, pp.426-434, 2008.
  11. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," in Proceedings of the 26th International Conference on Neural Information Processing Systems, Vol.2, pp. 3111-3119, 2013.
  12. O. Barkan and Koenigstein, M. "Item2vec: Neural item embedding for collaborative filtering," In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, pp.1-6, 2016.
  13. A. Grover and J. Leskovec, "node2vec: Scalable feature learning for networks," In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.855-864, 2016.
  14. J. Masci, U. Meier, D. Ciresan, and J. Schmidhuber, "Stacked convolutional auto-encoders for hierarchical feature extraction," In Proceedings of the International Conference on Artificial Neural Networks, pp.52-59, 2011.
  15. H. Steck, "Embarrassingly shallow autoencoders for sparse data," In Proceedings of the World Wide Web Conference, pp.3251-3257, 2019.
  16. D. P. Kingma, and J. Ba, "Adam: A method for stochastic optimization," In Proceedings of the International Conference on Learning Representations, 2015.