A bias detection tree approach for detecting disparities in a recommendation model’s errors-Reference-Cited by-同舟云学术

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A bias detection tree approach for detecting disparities in a recommendation model’s errors

Published:2022-07-11 Issue:1 Volume:33 Page:43-79
ISSN:0924-1868
Container-title:User Modeling and User-Adapted Interaction
language:en
Short-container-title:User Model User-Adap Inter

Author:

Misztal-Radecka Joanna^ORCID,Indurkhya Bipin

Funder

Ministerstwo Edukacji i Nauki

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Human-Computer Interaction,Education

Link

https://link.springer.com/content/pdf/10.1007/s11257-022-09334-x.pdf

Reference50 articles.

1. Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning-to-rank recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys’17, pp 42–46. Association for Computing Machinery, New York (2017)

2. Anelli, V.W., Di Noia, T., Di Sciascio, E., Ragone, A., Trotta, J.: Local popularity and time in top-n recommendation. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds.) Advances in Information Retrieval, pp. 861–868. Springer, Cham (2019)

3. Baeza-Yates, R.: Bias on the web. Commun. ACM 61, 54–61 (2018)

4. Barocas, S., Hardt, M., Narayanan, A.: Fairness and Machine Learning. fairmlbook.org (2019). http://www.fairmlbook.org

5. Beutel, A., Chi, E.H., Cheng, Z., Pham, H., Anderson, J.: Beyond globally optimal: Focused learning for improved recommendations. In: Proceedings of the 26th International Conference on World Wide Web, WWW’17, pp. 203–212, Republic and Canton of Geneva, CHE, 2017. International World Wide Web Conferences Steering Committee

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1. Integrating Social Environment in Machine Learning Model for Debiased Recommendation;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

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