ReCon: Reducing Congestion in Job Recommendation using Optimal Transport-Reference-Cited by-同舟云学术

ReCon: Reducing Congestion in Job Recommendation using Optimal Transport

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

Author:

Mashayekhi Yoosof¹^ORCID,Kang Bo¹^ORCID,Lijffijt Jefrey¹^ORCID,De Bie Tijl¹^ORCID

Affiliation:

1. Ghent University, Belgium

Funder

Fonds Wetenschappelijk Onderzoek

European Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604915.3608817

Reference18 articles.

1. A review on deep learning for recommender systems: challenges and remedies

2. Guillaume Bied , Elia Perennes , Victor Naya , Philippe Caillou , Bruno Crépon , Christophe Gaillac , and Michele Sebag . 2021 . Congestion-Avoiding Job Recommendation with Optimal Transport. In FEAST workshop ECML-PKDD 2021. Guillaume Bied, Elia Perennes, Victor Naya, Philippe Caillou, Bruno Crépon, Christophe Gaillac, and Michele Sebag. 2021. Congestion-Avoiding Job Recommendation with Optimal Transport. In FEAST workshop ECML-PKDD 2021.

3. Toward Fair Recommendation in Two-sided Platforms

4. LiJAR

5. Kuan-Ming Chen , Yu-Wei Hsieh , and Ming-Jen Lin . 2019. Prediction and congestion in two-sided markets: Economist versus machine matchmakers. SSRN Electronic Journal ( 2019 ). Kuan-Ming Chen, Yu-Wei Hsieh, and Ming-Jen Lin. 2019. Prediction and congestion in two-sided markets: Economist versus machine matchmakers. SSRN Electronic Journal (2019).

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