Optimizing Base Rankers Using Clicks: A Case Study using BM25

Abstract

We study the problem of optimizing an individual base ranker using clicks. Surprisingly, while there has been considerable attention for using clicks to optimize linear combinations of base rankers, the problem of optimizing an individual base ranker using clicks has been ignored. The problem is different from the problem of optimizing linear combinations of base rankers as the scoring function of a base ranker may be highly non-linear. For the sake of concreteness, we focus on the optimization of a specific base ranker, viz. BM25. We start by showing that significance improvements in performance can be obtained when optimizing the parameters of BM25 for individual datasets. We also show that it is possible to optimize these parameters from clicks, i.e., without the use of manually annotated data, reaching or even beating manually tuned parameters.