#recommender-systems

[ follow ]
#knowledge-sharing

New Framework for Deep Mutual Learning (DML) to Improve Multi-task Recommender Systems | HackerNoon

Multi-task recommender systems benefit from integrating knowledge sharing across tasks rather than using standalone architectures.

Deep Mutual Learning Optimizes Multi-Task Recommender Systems with Cross Task Feature Mining | HackerNoon

DML enhances upper-level networks in multi-objective ranking for improved recommender system performance.

New Framework for Deep Mutual Learning (DML) to Improve Multi-task Recommender Systems | HackerNoon

Multi-task recommender systems benefit from integrating knowledge sharing across tasks rather than using standalone architectures.

Deep Mutual Learning Optimizes Multi-Task Recommender Systems with Cross Task Feature Mining | HackerNoon

DML enhances upper-level networks in multi-objective ranking for improved recommender system performance.
moreknowledge-sharing
#machine-learning

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.

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.

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.

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.
moremachine-learning

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.
[ Load more ]