Explore the world of online personalized recommendations through a deep dive into Deep Bayesian Bandits in this 29-minute video lecture. Delve into Contextual Bandit Theory, examining Upper Confidence Bound (UCB) and Thompson Sampling techniques. Gain insights into various approaches including Epsilon Greedy, bootstrapping, dropout method, and hybrid methods. Analyze experimental setups and results, considering factors such as time-to-click delay and fake negatives. Enhance your understanding of advanced recommendation algorithms and their practical applications in online personalization.