Handling Feedback Loops in Recommender Systems — Deep Bayesian Bandits

The post explores how feedback loops in recommender systems lead to the cold-start problem and introduces Deep Bayesian Bandits as a solution. It covers various exploration techniques such as ε-greedy policy, Upper Confidence Bound (UCB), and Thompson sampling. The post also delves into methods for capturing uncertainty in neural networks like bootstrapping, multi-head architectures, and dropout techniques. Real-world experimentation is emphasized for optimal results.