Recommendation fairness and where to find it: An empirical study on fairness of user recommender systems-Reference-Cited by-同舟云学术

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Recommendation fairness and where to find it: An empirical study on fairness of user recommender systems

Published:2023-12-15 Issue: Volume:abs/2103.14748 Page:4195-4204
ISSN:
Container-title:2023 IEEE International Conference on Big Data (BigData)
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Author:

Tommasel Antonela¹,Assent Ira²

Affiliation:

1. Aarhus University,Department of Computer Science DIGIT Aarhus University Centre for Digitalisation, Big Data and Data Analytics, Denmark ISISTAN, CONICET-UNCPBA,Argentina

2. Aarhus University,Department of Computer Science DIGIT Aarhus University Centre for Digitalisation, Big Data and Data Analytics,Denmark

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10385234/10386078/10386616.pdf?arnumber=10386616

Reference49 articles.

1. A flexible framework for evaluating user and item fairness in recommender systems

2. Bias and Debias in Recommender System: A Survey and Future Directions

3. Fairness in Information Access Systems

4. The Interaction between Political Typology and Filter Bubbles in News Recommendation Algorithms

5. Fairness in recommender systems: research landscape and future directions

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Analysing the Effect of Recommendation Algorithms on the Spread of Misinformation;ACM Web Science Conference;2024-05-21

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