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. LEMONS (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. HaunschmidāThese authors contributed equally.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
Details about training and architecture can be found in our GitHub repository.
- 3.
References
Tintarev, N., Masthoff, J.: Explaining recommendations: design and evaluation. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 353ā382. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_10
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. 329ā338. Association for Computing Machinery (2020)
Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82ā115 (2020)
Zhang, Y., Chen, X.: Explainable recommendation: a survey and new perspectives. Found. Trends Inf. Retrieval 14(1), 1ā101 (2020)
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. 83ā92. Association for Computing Machinery (2014)
Tsukuda, K., Goto, M.: Explainable recommendation for repeat consumption. In: 14th ACM Conference on Recommender Systems, pp. 462ā467. Association for Computing Machinery (2020)
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. 345ā354. Association for Computing Machinery (2017)
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. 175ā182. Association for Computing Machinery (2016)
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. 765ā774. Association for Computing Machinery (2019)
Kouki, P., Schaffer, J., Pujara, J., OāDonovan, J., Getoor, L.: User preferences for hybrid explanations. In: Proceedings of the 11th ACM Conference on Recommender Systems, pp. 84ā88. Association for Computing Machinery (2017)
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. 241ā250. Association for Computing Machinery (2000)
Vig, J., Sen, S., Riedl, J.: Tagsplanations: explaining recommendations using tags. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, pp. 47ā56. Association for Computing Machinery (2009)
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. 329ā338. Association for Computing Machinery (2009)
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. 397ā407. Association for Computing Machinery (2019)
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. 1133ā1144. Association for Computing Machinery (2013)
Zhao, G., et al.: Personalized reason generation for explainable song recommendation. ACM Trans. Intell. Syst. Technol. 10(4), 1ā21 (2019)
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. 5329ā5336. Association for the Advancement of Artificial Intelligence Press (2019)
Haunschmid, V., Manilow, E., Widmer, G.: audioLIME: listenable explanations using source separation. In: 13th International Workshop on Machine Learning and Music, pp. 20ā24 (2020)
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)
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. 805ā811 (2016)
Pan, R., et al.: One-class collaborative filtering. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 502ā511. Institute of Electrical and Electronics Engineers (2008)
Ribeiro, M.T., Singh, S., Guestrin, C.: āWhy 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. 1135ā1144. Association for Computing Machinery (2016)
Haunschmid, V., Manilow, E., Widmer, G.: Towards Musically Meaningful Explanations Using Source Separation. CoRR abs/2009.02051 (2020). https://arxiv.org/abs/2009.02051
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. 591ā596. University of Miami (2011)
Rafii, Z., Liutkus, A., Stƶter, F.R., Mimilakis, S.I., Bittner, R.: MUSDB18 - A Corpus for Music Separation (2017)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2021 Springer Nature Switzerland AG
About this paper
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)