By W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.)
This publication constitutes the refereed lawsuits of the thirtieth annual eu convention on info 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 rigorously reviewed and chosen from 188 submissions. The papers are geared up in topical sections on retrieval version, collaborative IR / filtering, studying, multimedia - metadata, specialist seek - ads, assessment, opinion detection, internet IR, illustration, clustering / categorization in addition to dispensed IR.
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Additional resources for Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings
Process. Manage. 42(1), 31–55 (2006) 21. : Improving recommendation lists through topic diversiﬁcation. In: WWW 2005 (2005) Risk-Aware Information Retrieval Jianhan Zhu1 , Jun Wang1 , Michael Taylor2 , and Ingemar J. K. K. com Abstract. Probabilistic retrieval models usually rank documents based on a scalar quantity. However, such models lack any estimate for the uncertainty associated with a document’s rank. Further, such models seldom have an explicit utility (or cost) that is optimized when ranking documents.
To train LDA and PAM, because exact inference is intractable, we utilized the Gibbs Sampling approach [3,7] for approximate inference. The training complexity of diﬀerent inference methods have been analyzed elsewhere [3,4,7]. Note that training sophisticated topic models like LDA and PAM is much more computationally expensive than training MU. 2GHz CPU, 4GB memory and Linux OS: an 800-topics MU took about 8 hours to ﬁnish 25 iterations (converged); an 800-topics LDA took 7 days and 14 hours to ﬁnish 1000 iterations (converged); an 800 sub-topics and 100 super-topics PAM took 18 days and 6 hours to ﬁnish 100 iterations (not converged).
We show that: (1) topic models are eﬀective for document smoothing; (2) more rigorous topic models such as Latent Dirichlet Allocation provide gains over cluster-based models; (3) more elaborate topic models that capture topic dependencies provide no additional gains; (4) smoothing documents by using their similar documents is as eﬀective as smoothing them by using topic models; (5) doing query expansion should utilize topics discovered in the top feedback documents instead of coarse-grained topics from the whole corpus; (6) generally, incorporating topics in the feedback documents for building relevance models can beneﬁt the performance more for queries that have more relevant documents.
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.)