@inproceedings{
yadav2024gaussian,
title={Gaussian Process Bandits for Top-k Recommendations},
author={Mohit Yadav and Cameron N Musco and Daniel Sheldon},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=50nEnmVLRb}
}
Algorithms that utilize bandit feedback to optimize top-k recommendations are vital for online marketplaces, search engines, and content platforms.  However, the combinatorial nature of this problem poses a significant challenge,  as the possible number of ordered top-k recommendations from $n$ items grows exponentially with $k$. As a result, previous work often relies on restrictive assumptions about the reward or bandit feedback models, such as assuming that the feedback discloses rewards for each recommended item rather than a single scalar feedback for the entire set of top-k recommendations. We introduce a novel contextual bandit algorithm for top-k recommendations, leveraging a Gaussian process with a Kendall kernel to model the reward function.
Our algorithm requires only scalar feedback from 
the top-k recommendations and does not impose restrictive assumptions on the reward structure. 
Theoretical analysis confirms that the proposed algorithm achieves sub-linear regret in relation to the number of rounds and arms. Additionally, empirical results using a bandit simulator demonstrate that the proposed algorithm outperforms other baselines across various scenarios.
Gaussian Process Bandits for Top-k Recommendations
Mohit Yadav Cameron N Musco Daniel Sheldon