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 lawsuits of the thirtieth annual eu convention on info Retrieval examine, 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 prepared in topical sections on overview, net IR, social media, cross-lingual info retrieval, concept, video, illustration, wikipedia and e-books, in addition to specialist search.

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The expected value of a coin flip), simulating an assessor that makes a random judgment about which of two similar pages is preferred. Statistical significance between Tables 2(a) and 2(b) can be measured by a χ2 test comparing the ratio of the number of pairs agreed on to the number disagreed on for both preferences and inferred preferences. The difference is significant (χ2 = 143, df = 1, p ≈ 0). We can also explore redistributing ties at different rates to model different levels of agreement. Up to about 70% agreement on ties, true preference agreement is still significantly greater than inferred preference agreement.

This research was carried out while the author worked at Sensis Pty Ltd. C. Macdonald et al. ): ECIR 2008, LNCS 4956, pp. 28–39, 2008. c Springer-Verlag Berlin Heidelberg 2008 Using Clicks as Implicit Judgments: Expectations Versus Observations 29 While the notion that click behaviour is indicative of user preferences is intuitively appealing, and has in previous work been shown to have some success in improving search results, many properties of clickthrough data have not been explored in detail.

These capture the transitive judgments discussed in the paragraph above. The weights wn (i, j) are set for an evaluation measure: wn (i, j) = 1 gives the utility function for ppref, while wn (i, j) = 1/ log2 (min{rn (i), rn (j)} + 1) (where rn (i) is the rank of document i by system n) produces the utility function for wpref. Note that the expected utility relies on an estimate of the probability that A is preferred to B. We assume a priori that this probability is 12 . After we have made some judgments involving A and some judgments involving B, we may have more information.

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