By W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.)
This booklet constitutes the refereed lawsuits of the thirtieth annual ecu convention on info Retrieval learn, 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 rigorously reviewed and chosen from 188 submissions. The papers are equipped in topical sections on retrieval version, collaborative IR / filtering, studying, multimedia - metadata, specialist seek - advertisements, review, opinion detection, net IR, illustration, clustering / categorization in addition to dispensed IR.
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Extra info for Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings
To train MU, we utilized an eﬃcient document clustering approach , which ﬁrst clusters documents by using any clustering algorithm like K-means, then estimates a multinomial distribution for each cluster. 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.
Risk-aversion (conservative ranking) we have a much greater chance that at least one document will be relevant, but the chance that many of the documents will be relevant is diminished. Conversely, for a risk-loving (aggressive ranking), we have a much greater chance that many of the documents will be relevant, but at the expense that some searches produce no relevant documents. This supports our discussion in Section 1 in which we described how a risk-loving strategy will lead to more volatility in our search results, but that the beneﬁt of this volatility is that for some searches, we will display more relevant documents.
The last component resembles the MMR, providing a theoretical justiﬁcation. 3 Related Work and Discussion In information retrieval, the most relevant work can be found in , where Chen and Karger argued that the classic probabilistic ranking principle (PRP) , which ranks documents in descending order of probability of relevance, is not always optimal for diﬀerent user information needs. In some scenarios users would be satisﬁed with a limited number of relevant documents, rather than all relevant documents.