Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model

As the underlying ranking policies constantly evolve, recommendation providers need to experiment offline with new approaches for ranking content before actually deploying and exposing them to the users [5, 6]. This serves the purpose of deploying only policies that have a large chance of improving the user experience. Deployed ranking policies provide a plethora of interaction logs that can be repurposed to learn and evaluate potentially better policies offline. These logs come in the form of implicit feedback, i.e., records of past interaction behavior, linked to information about the user, the context and the items to recommend. Off-policy evaluation of new policies on historic data requires adequate strategies to deal with biases coming from (i) the nature of user interaction and (ii) the logging policy. A prominent example of these biases is position bias [7] (content that is not ranked in the most visible positions is less likely to be seen). We focus on two popular classes of estimators that take different approaches to correcting for presentation bias. The first class, in the case of full visibility of all items, does not rely on explicit randomization, but models the randomness in user behavior. The most common model is the position-based model (PBM), which assumes that observed clicks on content factorize into relevance (depending on the item only) and the visibility of the content (depending on the position only).