Advances in Information Retrieval: 31th European Conference - download pdf or read online

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 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 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, overview, opinion detection, internet IR, illustration, clustering / categorization in addition to disbursed 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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14 J. 08 0 10 20 30 40 50 α 60 70 80 90 100 0 10 20 30 (a) 40 50 α 60 70 80 90 100 (b) Fig. 3. The impact of the parameter α. (a) NDCG at 3 and (b) Mean reciprocal rank. 2 Performance Evaluation This section compares our ranking principle with the PRP-based ranking. For our approach, we set α = 30. Notice that the setting of the parameter is not optimal for all the configurations. But we intend to investigate the performance of ranking method by using a universal value of α. Given this, it is expected that the performance can be improved even further when α is optimized with respect to individual collections or metrics.

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. : Latent semantic models for collaborative filtering. ACM Trans. Info. Syst. 22(1), 89–115 (2004) 7. : Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002) 8. : A study of mixture models for collaborative filtering.

The impact of the parameter α. (a) NDCG at 3 and (b) Mean reciprocal rank. 2 Performance Evaluation This section compares our ranking principle with the PRP-based ranking. For our approach, we set α = 30. Notice that the setting of the parameter is not optimal for all the configurations. But we intend to investigate the performance of ranking method by using a universal value of α. Given this, it is expected that the performance can be improved even further when α is optimized with respect to individual collections or metrics.

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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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