Summary of A Personalized Framework For Consumer and Producer Group Fairness Optimization in Recommender Systems, by Hossein A. Rahmani et al.
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems
by Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes CP-FairRank, an optimization-based re-ranking algorithm that integrates fairness constraints from both consumer and producer sides in a joint objective framework. The algorithm is designed to handle varied fairness settings based on group segmentation, recommendation model selection, and domain. It demonstrates the ability to improve both consumer and producer fairness without compromising overall recommendation quality, highlighting the role algorithms can play in avoiding data biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop an algorithm called CP-FairRank that helps make recommendations fair for both people who use them (consumers) and those whose items are recommended (producers). This is important because current recommender systems might treat certain groups unfairly. The algorithm considers different fairness settings and can improve fairness without sacrificing how well the recommendations work overall. |
Keywords
» Artificial intelligence » Optimization
