The Best Published Result is Random
Ben Carterette · 2015
Reusable test collections allow researchers to rapidly test different algorithms to find the one that works "best". But because of randomness in the topic sample, or in relevance judgments, or in interactions among system components, extreme results can be seen entirely due to chance, particularly when a collection becomes very popular. We argue that the best known published effectiveness on any given collection could be measured as much as 20% higher than its "true" intrinsic effectiveness, and that there are many other systems with lower measured effectiveness that could have substantially higher intrinsic effectiveness.