Advances in Information Retrieval: 30th European Conference by Nicholas J. Belkin (auth.), Craig Macdonald, Iadh Ounis,

By Nicholas J. Belkin (auth.), Craig Macdonald, Iadh Ounis, Vassilis Plachouras, Ian Ruthven, Ryen W. White (eds.)

This e-book constitutes the refereed complaints of the thirtieth annual ecu convention on details Retrieval learn, ECIR 2008, held in Glasgow, united kingdom, in March/April 2008.

The 33 revised complete papers and 19 revised brief papers provided including the abstracts of three invited lectures and 32 poster papers have been conscientiously reviewed and chosen from 139 complete article submissions. The papers are prepared in topical sections on assessment, net IR, social media, cross-lingual details retrieval, conception, video, illustration, wikipedia and e-books, in addition to specialist search.

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Järvelin et al. Concatenated Top-10 Results: Average sDCG in Scenario D 60,00 50,00 40,00 sDCG S-1-T-10 S-1-T-10/4Q 30,00 S-2-T-10 S-2-T-10/4Q 20,00 10,00 0,00 0 10 20 30 40 50 60 Session Rank Fig. 3. Session performance by concatenated Top-10 results averaged across all sessions of Scenario D in Systems 1 (S-1-T-10) and 2 (S-2-T-10) and across the sessions that constructed at least four queries (S-1-T-10/4Q and S-2-T-10/4Q) (b=2; bq=4) sDCG in Individual Sessions, Top-10, System 1 120,00 100,00 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 sDCG 80,00 60,00 40,00 20,00 0,00 0 10 20 30 40 50 60 Concatenated Session Rank Fig.

Instead of assigning a relevance label to a document, an assessor looks at two pages and expresses a preference for one over the other. This is a binary decision, so there is no need to determine a set of labels and no need to map judgments to a numeric scale. Of course, using preference judgments poses a new set of questions: how do we use preference judgments to evaluate a search engine? The number of pairs of documents is polynomial in the number of documents; will it be feasible to ask for judgments on every pair?

When averaging gain across multiple search scenarios and using independent relevance judgments, normalizing (see Appendix) adjusts for the differing number of relevant documents across scenarios and represents performance in the range [0, 1] in relation to the ideal. Thence statistical testing is not affected by outliers, scenarios returning many highly relevant documents. S. National Science Foundation, grant numbers 0514238, 0511050 and 0534762. References 1. : The design of Browsing and Berrypicking Techniques for the Online Search Interface.

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