ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations-Reference-Cited by-同舟云学术

ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations

Published:2022-09-18 Issue: Volume: Page:
ISSN:
Container-title:Sixteenth ACM Conference on Recommender Systems
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Author:

Melchiorre Alessandro B.¹,Rekabsaz Navid¹,Ganhör Christian²,Schedl Markus¹

Affiliation:

1. Institute of Computational Perception & Multimedia Mining and Search Group, Johannes Kepler University Linz, Austria and Human-centered AI Group, AI Lab, Linz Institute of Technology, Austria

2. Institute of Computational Perception & Multimedia Mining and Search Group, Johannes Kepler University Linz, Austria

Funder

Austrian Science Fund (FWF)

the State of Upper Austria and the Federal Ministry of Education, Science, and Research

Publisher

ACM

Reference71 articles.

1. Explainability in music recommender systems

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3. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

4. Ricardo Baeza-Yates . 2020. Bias in Search and Recommender Systems . Association for Computing Machinery , New York, NY, USA , 2. https://doi.org/10.1145/3383313.34 1843 5 Ricardo Baeza-Yates. 2020. Bias in Search and Recommender Systems. Association for Computing Machinery, New York, NY, USA, 2. https://doi.org/10.1145/3383313.3418435

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