By W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.)
This ebook constitutes the refereed lawsuits of the thirtieth annual eu convention on details Retrieval learn, ECIR 2009, held in Toulouse, France in April 2009.
The forty two revised complete papers and 18 revised brief papers offered including the abstracts of three invited lectures and 25 poster papers have been conscientiously reviewed and chosen from 188 submissions. The papers are geared up in topical sections on retrieval version, collaborative IR / filtering, studying, multimedia - metadata, professional seek - ads, assessment, opinion detection, internet IR, illustration, clustering / categorization in addition to disbursed 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
Pattern Recognition and Machine Learning. Springer, Heidelberg (2006) 2. : The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998 (1998) 3. : Less is more: probabilistic models for retrieving fewer relevant documents. In: SIGIR 2006 (2006) 4. : A utility theoretic examination of the probability ranking principle in information retrieval. JASIS 42(10), 703–714 (1991) 5. : An algorithmic framework for performing collaborative ﬁltering. In: SIGIR 1999 (1999) 6.
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 ). Further details can be found in Appendix A.
P(Di ) Di ∈C (2) n=1 Comparing this equation with Equation (1), it can be seen that the generative process in RM can be viewed as an unusual MU topic modeling approach that treats each document Di as the representative of its own topic ti . The model-based feedback (MFB) approach  assumes feedback documents related to a query q are generated through a two-component mixture model, of which one component is the background topic θC and the other component is a query dependent topic θq . To generate each feedback document, for each word, MFB ﬁrst picks either θq or θC to generate this word, then samples the word from the selected topic.
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.)