Collaborative Filtering Prone to Popularity Bias, Resulting in Overrepresentation of Popular Items in the Recommendation Outputs
Description
Collaborative filtering prone to popularity bias, resulting in overrepresentation of popular items in the recommendation outputs.
Actors
Alleged Deployers
- youtube
- netflix
Alleged Developers
- youtube
- netflix
Harmed Parties
- facebook-users
- linkedin-users
- youtube-users
- netflix-users
- x-(twitter)-users
Classification
Risk Domain
Discrimination & ToxicityRisk Subdomain
1.3. Unequal performance across groups
MIT Risk Profile
Topics
Source
Related Incidents (Discrimination & Toxicity)
View all →Research Papers on This Topic
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Mitigation Tools
Tools related to this risk domain, via shared responsible AI topics.
Open Source
View all →AIF360
Trusted-AI/AIF360
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
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fairlearn
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
⭐ 2,199
responsible-ai-toolbox
microsoft/responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.