The other day, I received a surprise package in the mail: a copy of IBM researchers David Carmel and Elad Yom-Tov‘s newly published lecture on “Estimating the Query Difficulty for Information Retrieval“. I wasn’t even aware that this book was being written, so I’m especially appreciative of the publisher’s kindness to send me a copy.
If you liked Claudia Hauff‘s recent dissertation on “Predicting the Effectiveness of Queries and Retrieval Systems” (cf. my blog post on how “Not All Queries Are Created Equal“), then you’ll love this compact lecture that review the work on pre-retrieval and post-retrieval prediction of query performance. It covers query clarity, ranking robustness, query coherence, and much more.
I’m a big fan of the Morgan & Claypool series of Synthesis Lectures on Information Concepts, Retrieval, and Services, though I’m admittedly biased. Still, I think these books are an excellent way to get an overview of a subject, and Carmel and Yom-Tov’s book delivers wonderfully. For those not lucky enough to receive free copies in the mail, I recommend Amazon, which is selling it for less than $24.
The Noisy Channel 
3 replies on “Estimating the Query Difficulty for Information Retrieval”
Bravo to David Carmel and Elad Yom-Tov!
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[…] prediction of query difficulty did not correlate (or at best correlated weakly) to post-retrieval query performance prediction (QPP) measures like query clarity. I talked with Diane about it, and I wonder how strongly the […]
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[…] retrieval models and learning to rank. I most enjoyed the two talks by Oren Kurland that focused on query performance prediction. In particular, he offered a comprehensive probabilistic prediction framework that unifies most of […]
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