Proceedings of the 2016 ACM on International Conference on the Theory of Information Retrieval
Pool bias is a well understood problem of test-collection based benchmarking in information retrieval. The pooling method itself is designed to identify all relevant documents. In practice, 'all' translates to `as many as possible given some budgetary constraints' and the problem persists, albeit mitigated. Recently, methods to address this pool bias for previously created test collections have been proposed, for the evaluation measure precision at cut-off (P@n). Analyzing previous methods, we make the empirical observation that the distribution of the probability of providing new relevant documents to the pool, over the runs, is log-normal (when the pooling strategy is fixed depth at cut-off). We use this observation to calculate a prior probability of providing new relevant documents, which we then use in a pool bias estimator that improves upon previous estimates of precision at cut-off. Through extensive experimental results, covering 15 test collections, we show that the proposed bias correction method is the new state of the art, providing the closest estimates yet when compared to the original pool.
Information and Communication Technology