Skip to yearly menu bar Skip to main content


Poster

Overcoming Prior Misspecification in Online Learning to Rank

Javad Azizi · Ofer Meshi · Masrour Zoghi · Maryam Karimzadehgan

Auditorium 1 Foyer 50

Abstract:

The recent literature on online learning to rank (LTR) has established the utility of prior knowledge to Bayesian ranking bandit algorithms. However, a major limitation of existing work is the requirement for the prior used by the algorithm to match the true prior.In this paper, we propose and analyze adaptive algorithms that address this issue and additionally extend these results to the linear and generalized linear models. We also consider scalar relevance feedback on top of click feedback.Moreover, we demonstrate the efficacy of our algorithms using both synthetic and real-world experiments.

Live content is unavailable. Log in and register to view live content