Explainability in music recommender systems-Reference-Cited by-同舟云学术

Explainability in music recommender systems

Published:2022-06 Issue:2 Volume:43 Page:190-208
ISSN:0738-4602
Container-title:AI Magazine
language:en
Short-container-title:AI Magazine

Author:

Afchar Darius¹^ORCID,Melchiorre Alessandro B.²^ORCID,Schedl Markus²^ORCID,Hennequin Romain¹^ORCID,Epure Elena V.¹^ORCID,Moussallam Manuel¹^ORCID

Affiliation:

1. Deezer Research Paris France

2. Johannes Kepler University and Linz Institute of Technology Linz Austria

Funder

Austrian Science Fund

Publisher

Wiley

Subject

Artificial Intelligence

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12056

Reference88 articles.

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2. Adebayo J. J.Gilmer M.Muelly I.Goodfellow M.Hardt andB.Kim.2018. “Sanity Checks for Saliency Maps.” InProceedings of the Advances in Neural Information Processing Systems31 9505–15.

3. Afchar D. andR.Hennequin.2020. “Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction.” InProceedings of the Fourteenth ACM Conference on Recommender Systems 220–9.New York:ACM.

4. Aljanaki A. andM.Soleymani.2018. “A Data‐driven Approach to Mid‐level Perceptual Musical Feature Modeling.” InProceedings of the International Society for Music Information Retrieval Conference.

5. Alvarez‐Melis D. andT. S.Jaakkola.2018. “On the Robustness of Interpretability Methods.” InICML Workshop on Human Interpretability in Machine Learning.

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