Promoting Two-sided Fairness with Adaptive Weights for Providers and Customers in Recommendation
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- Promoting Two-sided Fairness with Adaptive Weights for Providers and Customers in Recommendation
Recommendations
Intersectional Two-sided Fairness in Recommendation
WWW '24: Proceedings of the ACM Web Conference 2024Fairness of recommender systems (RS) has attracted increasing attention recently. Based on the involved stakeholders, the fairness of RS can be divided into user fairness, item fairness, and two-sided fairness which considers both user and item fairness ...
Fairness-aware Cross-Domain Recommendation
Database Systems for Advanced ApplicationsAbstractCross-Domain Recommendation (CDR) is an effective approach for mitigating the cold-start problem. However, previous research largely overlooks the issue of fairness: undesired bias associated with sensitive attributes in CDR scenarios. ...
Fairness-Aware Recommendation in Multi-Sided Platforms
WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data MiningFairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be important to optimize utilities not just for ...
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- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Association for Computing Machinery
New York, NY, United States
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- Short-paper
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- Refereed limited
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- Beijing Natural Science Foundation
- Building world-class universities (disciplines) of Renmin University of China
- National Natural Science Foundation of China
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