Estimating the Query Difficulty for Information Retrieval

David Carmel is a Research Staff Member at the Information Retrieval group at IBM Research Lab at Haifa. David earned his PhD in Computer Science from the Technion, Israel Institute of Technology in 1997. David's research is focused on search in the enterprise, query performance prediction, social search, and text mining. For several years David taught the Introduction to IR course at the CS department at Haifa university. At IBM, David is a key contributor to IBM enterprise search offerings. David is a co-founder of the Juru search engine which provides integrated search capabilities to several IBM products, and was used as a search platform for several studies in TREC conferences. David has published more than 60 papers in Information retrieval and Web journals and conferences, and he serves in the Program Committee of many conferences (SIGIR, WWW, WSDM, CIKM, ECIR), journals (IR Journal), and workshops. Elad Yom-Tov is a Research Staff Member at the Data Analytics department at IBMResearch Lab at Haifa, Israel. The main focus of his work is research into methods for large-scale machine learning, with a recent focus on social analytics. Prior to his current position he worked at Rafael Inc., where he applied machine learning to image processing. Elad is a graduate of Tel-Aviv University (B.Sc.) and the Technion, Haifa (M.Sc. and Ph.D). He is the author (with David Stork) of the Computer Manual to accompany Pattern Classification, a book and a Matlab toolbox on pattern classification. He has published over 40 papers on Machine Learning and its applications. Elad's work in Information Retrieval includes query difficulty estimation, social tagging, and novelty detection.

• Introduction - The Robustness Problem of Information Retrieval.- Basic Concepts.- Query Performance Prediction Methods.- Pre-Retrieval Prediction Methods.- Post-Retrieval Prediction Methods.- Combining Predictors.- A General Model for Query Difficulty.- Applications of Query Difficulty Estimation.- Summary and Conclusions.