A/B testing in information retrieval (IR) is an experimental approach where two versions of a system (version A and version B) are tested to compare their performance. Users are randomly divided into two groups, each interacting with one version of the system. The goal is to measure how changes in the IR system—such as adjustments to the ranking algorithm—affect user engagement and search result relevance.
In IR, A/B testing is often used to test new features, such as ranking algorithms, presentation formats, or personalized search results. For example, an IR system might compare a new ranking model (version B) against the current one (version A) to determine which provides better user satisfaction, measured by metrics like click-through rate (CTR) or conversion rate.
By measuring and comparing performance, developers can make data-driven decisions on which version of the system better meets user needs. This approach helps to continuously improve IR systems based on real user feedback and optimize the overall search experience.