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 publication constitutes the refereed court cases of the thirtieth annual ecu convention on info Retrieval learn, ECIR 2008, held in Glasgow, united kingdom, in March/April 2008.

The 33 revised complete papers and 19 revised brief papers offered including the abstracts of three invited lectures and 32 poster papers have been rigorously reviewed and chosen from 139 complete article submissions. The papers are geared up in topical sections on evaluate, net IR, social media, cross-lingual info retrieval, idea, video, illustration, wikipedia and e-books, in addition to professional search.

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This experimental framework is also at the core of the ongoing annual TREC conferences [21]. Retrieval metrics are usually based on a calculation of the precision of the search system (the number of relevant document retrieved as a proportion of the total number of documents that have been retrieved) and the recall (the number of relevant documents retrieved as a proportion of the total number of relevant document for that query). Precision therefore reflects the accuracy of the search results, while recall reflects the completeness.

However, a domain specific interface may support the searcher in (re)formulating a much better query. New tools, such as sDCG, are essential for evaluating such interfaces. sDCG can accept relevance scores derived from users, from independent relevance assessors, or from pre-existing test collections. For concatenating top-N results, there are three important issues to consider: • Scanning length: In this study, we assumed most users consider the top ten results. Ideally we want to know how far each user scanned each result list, using eyetracking data or having searchers mark the last document considered.

ACM Press, New York (2007) 9. : Evaluation by highly relevant documents. In: 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 74–82. ACM Press, New York (2001) Appendix The cumulated gain at ranked position i is computed by summing from position 1 to i, i ≤ 100. By denoting the value in position i in the gain vector G by G[i], the cumulated gain vector CG is defined as the vector CG where: CG[i] = ∑ i j=1 G[ j ] (1) For example, assuming G' = <3, 2, 3, 0, 0, 1, 2, 2, 3, 0, … > we obtain CG' = <3, 5, 8, 8, 8, 9, 11, 13, 16, 16, …>.

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