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Download e-book for kindle: Advances in Information Retrieval: 31th European Conference by W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut,

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 learn, 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 prepared in topical sections on retrieval version, collaborative IR / filtering, studying, multimedia - metadata, specialist seek - advertisements, overview, opinion detection, internet IR, illustration, clustering / categorization in addition to allotted IR.

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Read or Download Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings PDF

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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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A language modeling approach to information retrieval. In: SIGIR 1998 (1998) 13. : The probability ranking principle in IR. Readings in information retrieval, 281–286 (1997) 14. : Ranking in principle. Journal of Documentation, 93– 100 (1978) 15. : Item-based collaborative filtering recommendation algorithms. In: WWW 2001 (2001) 16. : A simplified model for portfolio analysis. Management Science 9(2) (1963) 17. : The TREC-8 question answering track report. In: TREC-8, pp. 77–82 (1999) 18. : Probabilistic relevance models for collaborative filtering.

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. We believe this is because background smoothing plays a similar role to our approach in relevance score adjustment.

The criterion has been applied to the recommendation problem in [21]. In text retrieval, the MMR criterion has also been employed in a risk framework to address the subtopic retrieval problem [9,20]. But nonetheless, 12 J. Wang when coming to the practical algorithms, these studies in [9,20] still resolve to take a point estimate, and use mode of the posterior without considering the uncertainty of the point estimate. Our work can be regarded as research along this direction, but set out for more ambitious goals.

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