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 ecu 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 conscientiously reviewed and chosen from 188 submissions. The papers are equipped in topical sections on retrieval version, collaborative IR / filtering, studying, multimedia - metadata, professional seek - advertisements, evaluate, opinion detection, net IR, illustration, clustering / categorization in addition to dispensed IR.
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Additional info for Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings
Table 2 (a) illustrates the relationship between the optimal values of b and ncall on the four collections. Table 2 (a) demonstrates that when n decreases, the optimal value of b tends to increase. This demonstrates how the risk adjustment Risk-Aware Information Retrieval 25 Table 3. Performance comparison on six metrics. Three lines in each cell are performance of a language model and our risk-aware approach, and the percentage of gain of our approach over the language model, respectively. Positive and statistically signiﬁcant improvements are in bold, and in bold and marked with “*”, respectively.
In this case, the relevance return of the list mimics that of the two documents. As a result, the list contains the same amount of uncertainty (risk) as those of the two documents. In this case, risk is not reduced. 4 Document Ranking – A Practical Solution Directly optimizing the objective function in Eq. (6) is computationally expensive. In this section, we present an eﬃcient document ranking algorithm by sequentially optimizing the objective function. It is based on the observation that the larger the rank of a relevant document, the less likely it would be seen or visited by the user.
Given a topic model T M , any document D and word w, w ∈ V, we ﬁrst calculate a topic model based document model pT M (w|D) by: pT M (w|ti )pT M (ti |D), pT M (w|D) = (5) ti ∈T where pT M (w|ti ) is the multinomial distribution in topic ti , pT M (ti |D) is the probability of observing topic ti in D, and T represents the topic set utilized to calculate this document model. T can either contain all the topics in model T M or just one topic tbest that a document D belongs to with the highest probability: tbest = arg maxti pT M (ti |D).