“Evaluating aggregated search using interleaving” by Aleksandr Chuklin (Google), Anne Schuth, Katja Hofmann (Microsoft Research), Pavel Serdyukov (Yandex) and Maarten de Rijke is available online now.

A result page of a modern web search engine is often much more complicated than a simple list of “ten blue links.” In particular, a search engine may combine results from different sources (e.g., Web, News, and Images), and display these as grouped results to provide a better user experience. Such a system is called an aggregated or federated search system.

Because search engines evolve over time, their results need to be constantly evaluated. However, one of the most efficient and widely used evaluation methods, interleaving, cannot be directly applied to aggregated search systems, as it ignores the need to group results originating from the same source (vertical results).

We propose an interleaving algorithm that allows comparisons of search engine result pages containing grouped vertical documents. We compare our algorithm to existing interleaving algorithms and other evaluation methods (such as A/B-testing), both on real-life click log data and in simulation experiments. We find that our algorithm allows us to perform unbiased and accurate interleaved comparisons that are comparable to conventional evaluation techniques. We also show that our interleaving algorithm produces a ranking that does not substantially alter the user experience, while being sensitive to changes in both the vertical result block and the non-vertical document rankings. All this makes our proposed interleaving algorithm an essential tool for comparing IR systems with complex aggregated pages.