By W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.)
This e-book constitutes the refereed court cases of the thirtieth annual ecu convention on details Retrieval study, ECIR 2009, held in Toulouse, France in April 2009.
The forty two revised complete papers and 18 revised brief papers provided including the abstracts of three invited lectures and 25 poster papers have been conscientiously reviewed and chosen from 188 submissions. The papers are equipped in topical sections on retrieval version, collaborative IR / filtering, studying, multimedia - metadata, specialist seek - ads, overview, opinion detection, internet IR, illustration, clustering / categorization in addition to dispensed IR.
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Extra info for Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings
The exact reason for the variation between MRR and MAP will be the subject of future work. We next compare to models using background smoothing. Fig. 1 (c) shows the performance gain on MRR for both Jelinek-Mercer and Dirichlet smoothing with diﬀerent parameters for the TREC8 collection. We can see from Fig. 1 (c) that a risk-aversion approach is eﬀective for all ﬁve diﬀerent background smoothing methods. However, increasing the inﬂuence of background smoothing via large values of λ or μ reduces the eﬀectiveness of risk adjustment.
1) follows previous work on considering both the probability of relevance and non-relevance in document ranking [4,10]. The logit model favours documents that are highly relevant for some terms in a multi-term query2 . The estimated mean and variance of the normal distribution obtained from Eq. (1) are given by E[f (θt )] ≈ ln θ¯t 2θ¯t − 1 + ¯2 V ar(θt ) ¯ 1 − θt 2θt (1 − θ¯t )2 V ar(θt ) , V ar[f (θt )] ≈ ¯2 θt (1 − θ¯t )2 (2) (3) where the mean and variance of θt are θ¯t and V ar(θt ), respectively, E[f (θt )] is the mean of f (θt ), and V ar[f (θt )] is the variance of f (θt ).
9 out of 24 results are statistically signiﬁcant, 22 out of 24 improvements are positive, and the highest improvement on MRR is over 25%, showing that risk adjustment can dramatically increase the chance of returning one relevant document close to the top of a ranked list. Our approach also outperforms the linear smoothing approach on MAP for all four collections. Comparing Table 3 (b) with Table 3 (a), we can see that our approach combined with the linear smoothing performs better than our approach without background smoothing in 18 out of 24 results, and better than the Dirichlet smoothing in 18 out of 24 results.
Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings by W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.)