Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation-Reference-Cited by-同舟云学术

Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation

Published:2023-09-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM Conference on Recommender Systems
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Author:

Moscati Marta¹^ORCID,Wallmann Christian²^ORCID,Reiter-Haas Markus³^ORCID,Kowald Dominik⁴^ORCID,Lex Elisabeth³^ORCID,Schedl Markus⁵^ORCID

Affiliation:

1. Institute of Computational Perception, Johannes Kepler University Linz, Austria

2. Welser Profile GmbH, Austria

3. Institute of Interactive Systems and Data Science, Graz University of Technology, Austria

4. FAIR-AI, Know-Center GmbH, Austria and Institute of Interactive Systems and Data Science, Graz University of Technology, Austria

5. Institute of Computational Perception, Johannes Kepler University Linz, Austria and Human-centered AI Group, AI Lab, Linz Institute of Technology, Austria

Funder

Austrian Science Fund

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

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604915.3608838

Reference53 articles.

1. Multistakeholder recommendation: Survey and research directions

2. Controlling Popularity Bias in Learning-to-Rank Recommendation

3. User-centered Evaluation of Popularity Bias in Recommender Systems

4. Explainability in music recommender systems

5. KATRec: Knowledge Aware aTtentive Sequential Recommendations

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