Online commercial app marketplaces serve millions of apps to billions of users in an efficient manner. Bandit optimization algorithms are used to ensure that the recommendations are relevant, and converge to the best performing content over time. However, directly applying bandits to real-world systems, where the catalog of items is dynamic and continuously refreshed, is not straightforward. One of the challenges we face is the existence of several competing content surfacing components, a phenomenon not unusual in large-scale recommender systems. This often leads to challenging scenarios…
We design new differentially private algorithms for the problems of adversarial bandits and bandits with expert advice. For adversarial bandits, we give a simple and efficient conversion of any non-private bandit algorithms to private bandit algorithms. Instantiating our conversion with existing non-private bandit algorithms gives a regret upper bound of…
*= Equal Contributors Recommendation systems in large-scale online marketplaces are essential to aiding users in discovering new content. However, state-of-the-art systems for item-to-item recommendation tasks are often based on a shallow level of contextual relevance, which can make the system insufficient for tasks where item relationships are more nuanced. Contextually…