Many information retrieval (IR) systems suffer from a radical variance in performance when responding to users' queries. Even
for systems that succeed very well on average, the quality of results returned for some of the queries is poor. Thus, it is
desirable that IR systems will be able to identify ""difficult"" queries so they can be handled properly. Understanding why
some queries are inherently more difficult than others is essential for IR, and a good answer to this important question will
help search engines to reduce the variance in performance, hence better servicing their customer needs. Estimating the query
difficulty is an attempt to quantify the quality of search results retrieved for a query from a given collection of documents.
This book discusses the reasons that cause search engines to fail for some of the queries, and then reviews recent approaches
for estimating query difficulty in the IR field. It then describes a common methodology for evaluating the prediction quality
of those estimators, and experiments with some of the predictors applied by various IR methods over several TREC benchmarks.
Finally, it discusses potential applications that can utilize query difficulty estimators by handling each query individually
and selectively, based upon its estimated difficulty.