#recommender-systems

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#machine-learning
Hackernoon
4 months ago
Data science

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Conclusion and References | HackerNoon

Niche users face bias in recommender systems; the study proposes solutions through an adaptive local learning framework. [ more ]
Hackernoon
4 months ago
Medicine

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Related Work | HackerNoon

Recommender systems face significant challenges due to fairness and bias, necessitating robust frameworks for addressing popularity, exposure, and demographic biases. [ more ]
Hackernoon
4 months ago
Data science

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Conclusion and References | HackerNoon

Niche users face bias in recommender systems; the study proposes solutions through an adaptive local learning framework. [ more ]
Hackernoon
4 months ago
Medicine

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Related Work | HackerNoon

Recommender systems face significant challenges due to fairness and bias, necessitating robust frameworks for addressing popularity, exposure, and demographic biases. [ more ]
moremachine-learning
Hackernoon
4 months ago
UX design

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Preliminaries | HackerNoon

The study addresses mainstream bias in recommender systems by aiming to improve utility for niche users while preserving utility for mainstream users. [ more ]
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