Exploiting Correlated Auxiliary Feedback in Parameterized Bandits
This work addresses online recommendation systems by leveraging readily available auxiliary data, though it appears incremental as it builds on existing parameterized bandits frameworks.
The paper tackles the parameterized bandits problem by incorporating correlated auxiliary feedback (e.g., service delivery time alongside user ratings) to improve reward estimation, resulting in reduced regret with performance gains verified experimentally.
We study a novel variant of the parameterized bandits problem in which the learner can observe additional auxiliary feedback that is correlated with the observed reward. The auxiliary feedback is readily available in many real-life applications, e.g., an online platform that wants to recommend the best-rated services to its users can observe the user's rating of service (rewards) and collect additional information like service delivery time (auxiliary feedback). In this paper, we first develop a method that exploits auxiliary feedback to build a reward estimator with tight confidence bounds, leading to a smaller regret. We then characterize the regret reduction in terms of the correlation coefficient between reward and its auxiliary feedback. Experimental results in different settings also verify the performance gain achieved by our proposed method.