Multiple-Play Bandits in the Position-Based Model
This work addresses the challenge of optimizing content placement in online platforms like search engines or recommendation systems, where position bias affects user engagement, and is incremental as it builds on existing multiple-play bandit models.
The paper tackles the problem of learning to place items in multi-position displays under the Position-Based Click Model, where user feedback may be ignored, by providing a novel regret lower bound and efficient algorithms with good empirical and theoretical performance.
Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the multiple-play semi-bandit setting. However, a major concern in this context is when the system cannot decide whether the user feedback for each item is actually exploitable. Indeed, much of the content may have been simply ignored by the user. The present work proposes to exploit available information regarding the display position bias under the so-called Position-based click model (PBM). We first discuss how this model differs from the Cascade model and its variants considered in several recent works on multiple-play bandits. We then provide a novel regret lower bound for this model as well as computationally efficient algorithms that display good empirical and theoretical performance.