Selectively Contextual Bandits
This work addresses the problem of optimizing user experience in personalization systems for industries by balancing personalization with community benefits, though it is incremental as it builds on existing bandit methods.
The paper tackles the trade-off between personalization and shared experience in contextual bandit systems by proposing a hybrid algorithm that selectively uses contextual information, showing benefits in balancing individualized and shared treatments in evaluations on public datasets.
Contextual bandits are widely used in industrial personalization systems. These online learning frameworks learn a treatment assignment policy in the presence of treatment effects that vary with the observed contextual features of the users. While personalization creates a rich user experience that reflect individual interests, there are benefits of a shared experience across a community that enable participation in the zeitgeist. Such benefits are emergent through network effects and are not captured in regret metrics typically employed in evaluating bandits. To balance these needs, we propose a new online learning algorithm that preserves benefits of personalization while increasing the commonality in treatments across users. Our approach selectively interpolates between a contextual bandit algorithm and a context-free multi-arm bandit and leverages the contextual information for a treatment decision only if it promises significant gains. Apart from helping users of personalization systems balance their experience between the individualized and shared, simplifying the treatment assignment policy by making it selectively reliant on the context can help improve the rate of learning in some cases. We evaluate our approach in a classification setting using public datasets and show the benefits of the hybrid policy.