Multi-Task Learning for Contextual Bandits
This work addresses personalized recommendation and ad placement by incrementally enhancing contextual bandit methods through multi-task learning.
The paper tackles the problem of improving reward prediction in contextual bandits by leveraging similarities between arms, proposing a multi-task learning framework with an upper confidence bound algorithm and establishing a regret bound to quantify benefits from high task similarity.
Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications. In this work, we propose a multi-task learning framework for contextual bandit problems. Like multi-task learning in the batch setting, the goal is to leverage similarities in contexts for different arms so as to improve the agent's ability to predict rewards from contexts. We propose an upper confidence bound-based multi-task learning algorithm for contextual bandits, establish a corresponding regret bound, and interpret this bound to quantify the advantages of learning in the presence of high task (arm) similarity. We also describe an effective scheme for estimating task similarity from data, and demonstrate our algorithm's performance on several data sets.