Bilinear Bandit

Bilinear bandits model scenarios where rewards depend on the interaction of two distinct entities, such as recommending items to users based on their combined preferences. Current research focuses on improving efficiency through multi-task learning, leveraging shared representations across multiple related problems to reduce the number of trials needed to find optimal pairings. Algorithms employing optimism in the face of uncertainty and experimental design approaches are being developed to address both pure exploration (finding the best pair) and regret minimization (minimizing cumulative reward loss) in increasingly complex settings, such as those involving interactions within a network of agents. These advancements hold promise for improving personalized recommendations and resource allocation in various applications.