W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut,'s Advances in Information Retrieval: 31th European Conference PDF

By W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.)

ISBN-10: 3642009573

ISBN-13: 9783642009570

ISBN-10: 3642009581

ISBN-13: 9783642009587

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

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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.

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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.)


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