Eugene Yan • 5/8/2022

Bandits for Recommender Systems

This technical article explains how bandit algorithms address the cold-start and feedback loop problems in recommender systems. It details three core algorithms—ε-greedy, Upper Confidence Bound (UCB), and Thompson Sampling—and discusses their industrial applications for dynamic item sets like news and ads, focusing on reducing regret through adaptive exploration.

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#Machine Learning #Reinforcement Learning #Recommender Systems