A Probabilistic Position Bias Model for Short-Video Recommendation Feeds

Abstract

Modern web-based platforms often show ranked lists of recommendations to users, in an attempt to maximise user satisfaction or business metrics. Typically, the goal of such systems boils down to maximising the exposure probability —conversely, minimising the rank— for items that are deemed “reward-maximising” according to some metric of interest. This general framing comprises music or movie streaming applications, as well as e-commerce, restaurant or job recommendations, and even web search. Position bias or user models can be used to estimate exposure probabilities for each use-case, specifically tailored to how users interact with the presented rankings. A unifying factor in these diverse problem settings is that typically only one or several items will be engaged with (clicked, streamed, purchased, et cetera) before a user leaves the ranked list.