Skip to main content

LEMONS: Listenable Explanations for Music recOmmeNder Systems

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2021)

Abstract

Although current music recommender systems suggest new tracks to their users, they do not provide listenable explanations of why a user should listen to them. LEMONSĀ (Demonstration video: https://youtu.be/giSPrPnZ7mc) is a new system that addresses this gap by (1) adopting a deep learning approach to generate audio content-based recommendations from the audio tracks and (2) providing listenable explanations based on the time-source segmentation of the recommended tracks using the recently proposed audioLIME.

A. B. Melchiorre and V. Haunschmidā€”These authors contributed equally.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/cpjku/lemons.

  2. 2.

    Details about training and architecture can be found in our GitHub repository.

  3. 3.

    https://www.7digital.com/.

References

  1. Tintarev, N., Masthoff, J.: Explaining recommendations: design and evaluation. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 353ā€“382. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_10

    ChapterĀ  Google ScholarĀ 

  2. Balog, K., Radlinski, F.: Measuring recommendation explanation quality: the conflicting goals of explanations. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 329ā€“338. Association for Computing Machinery (2020)

    Google ScholarĀ 

  3. Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82ā€“115 (2020)

    ArticleĀ  Google ScholarĀ 

  4. Zhang, Y., Chen, X.: Explainable recommendation: a survey and new perspectives. Found. Trends Inf. Retrieval 14(1), 1ā€“101 (2020)

    ArticleĀ  MathSciNetĀ  Google ScholarĀ 

  5. Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 83ā€“92. Association for Computing Machinery (2014)

    Google ScholarĀ 

  6. Tsukuda, K., Goto, M.: Explainable recommendation for repeat consumption. In: 14th ACM Conference on Recommender Systems, pp. 462ā€“467. Association for Computing Machinery (2020)

    Google ScholarĀ 

  7. Li, P., Wang, Z., Ren, Z., Bing, L., Lam, W.: Neural rating regression with abstractive tips generation for recommendation share on. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 345ā€“354. Association for Computing Machinery (2017)

    Google ScholarĀ 

  8. Chang, S., Harper, F.M., Terveen, L.G.: Crowd-based personalized natural language explanations for recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 175ā€“182. Association for Computing Machinery (2016)

    Google ScholarĀ 

  9. Chen, X., et al.: Personalized fashion recommendation with visual explanations based on multimodal attention network: towards visually explainable recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 765ā€“774. Association for Computing Machinery (2019)

    Google ScholarĀ 

  10. Kouki, P., Schaffer, J., Pujara, J., Oā€™Donovan, J., Getoor, L.: User preferences for hybrid explanations. In: Proceedings of the 11th ACM Conference on Recommender Systems, pp. 84ā€“88. Association for Computing Machinery (2017)

    Google ScholarĀ 

  11. Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, pp. 241ā€“250. Association for Computing Machinery (2000)

    Google ScholarĀ 

  12. Vig, J., Sen, S., Riedl, J.: Tagsplanations: explaining recommendations using tags. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, pp. 47ā€“56. Association for Computing Machinery (2009)

    Google ScholarĀ 

  13. Green, S.J., et al.: Generating transparent, steerable recommendations from textual descriptions of items. In: Proceedings of the 3rd ACM Conference on Recommender Systems, pp. 329ā€“338. Association for Computing Machinery (2009)

    Google ScholarĀ 

  14. Millecamp, M., Htun, N.N., Conati, C., Verbert, K.: To explain or not to explain: the effects of personal characteristics when explaining music recommendations. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 397ā€“407. Association for Computing Machinery (2019)

    Google ScholarĀ 

  15. Sharma, A., Cosley, D.: Do social explanations work? Studying and modeling the effects of social explanations in recommender systems. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1133ā€“1144. Association for Computing Machinery (2013)

    Google ScholarĀ 

  16. Zhao, G., et al.: Personalized reason generation for explainable song recommendation. ACM Trans. Intell. Syst. Technol. 10(4), 1ā€“21 (2019)

    ArticleĀ  Google ScholarĀ 

  17. Wang, X., Wang, D., Xu, C., He, X., Cao, Y., Chua, T.S.: Explainable reasoning over knowledge graphs for recommendation. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, vol. 33, pp. 5329ā€“5336. Association for the Advancement of Artificial Intelligence Press (2019)

    Google ScholarĀ 

  18. Haunschmid, V., Manilow, E., Widmer, G.: audioLIME: listenable explanations using source separation. In: 13th International Workshop on Machine Learning and Music, pp. 20ā€“24 (2020)

    Google ScholarĀ 

  19. Won, M., Ferraro, A., Bogdanov, D., Serra, X.: Evaluation of CNN-based automatic music tagging models. In: Proceedings of 17th Sound and Music Computing (2020)

    Google ScholarĀ 

  20. Choi, K., Fazekas, G., Sandler, M.: Automatic tagging using deep convolutional neural networks. In: Proceedings of the 17th International Conference on Music Information Retrieval (ISMIR 2016), pp. 805ā€“811 (2016)

    Google ScholarĀ 

  21. Pan, R., et al.: One-class collaborative filtering. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 502ā€“511. Institute of Electrical and Electronics Engineers (2008)

    Google ScholarĀ 

  22. Ribeiro, M.T., Singh, S., Guestrin, C.: ā€œWhy should i trust you?ā€: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135ā€“1144. Association for Computing Machinery (2016)

    Google ScholarĀ 

  23. Haunschmid, V., Manilow, E., Widmer, G.: Towards Musically Meaningful Explanations Using Source Separation. CoRR abs/2009.02051 (2020). https://arxiv.org/abs/2009.02051

  24. Bertin-Mahieux, T., Ellis, D.P., Whitman, B., Lamere, P.: The million song dataset. In: Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011), pp. 591ā€“596. University of Miami (2011)

    Google ScholarĀ 

  25. Rafii, Z., Liutkus, A., Stƶter, F.R., Mimilakis, S.I., Bittner, R.: MUSDB18 - A Corpus for Music Separation (2017)

    Google ScholarĀ 

  26. Hennequin, R., Khlif, A., Voituret, F., Moussallam, M.: Spleeter: a fast and efficient music source separation tool with pre-trained models. J. Open Source Softw. 5(50), 2154 (2020)

    ArticleĀ  Google ScholarĀ 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alessandro B. Melchiorre .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

Ā© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Melchiorre, A.B., Haunschmid, V., Schedl, M., Widmer, G. (2021). LEMONS: Listenable Explanations for Music recOmmeNder Systems. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_60

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72240-1_60

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72239-5

  • Online ISBN: 978-3-030-72240-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics