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.)

ISBN-10: 3642009573

ISBN-13: 9783642009570

ISBN-10: 3642009581

ISBN-13: 9783642009587

This booklet constitutes the refereed court cases of the thirtieth annual eu 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, professional seek - advertisements, evaluate, opinion detection, internet IR, illustration, clustering / categorization in addition to disbursed IR.

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4) under the language modelling framework. However, it is worth noting that the proposed method is generally applicable to any probabilistic retrieval models. , d|V | ), where qt (dt ) is the number of times the term t appears in the query (document) and |V | is the size of a vocabulary. , θ|V | ), where t θt = 1, and the probability space of θt is [0,1]. To estimate θt , a straightforward approach is to apply maximum likelihood estimation. However, estimating from one single document is unreliable due to small data samples.

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. Three popular recommendation algorithms were adopted to predict item ratings, namely the user-based algorithm [5], the item-based algorithm [15], and the method based on Probabilistic Latent Semantic Analysis (PLSA) [6].

Yi and J. qk }: first sample a document Di in the whole corpus C, then sample k + 1 times from its distribution p(w|Di ). We can follow the same process to generate a document D that has ND words and have: ND p(D) = p(wn |Di ). p(Di ) Di ∈C (2) n=1 Comparing this equation with Equation (1), it can be seen that the generative process in RM can be viewed as an unusual MU topic modeling approach that treats each document Di as the representative of its own topic ti . The model-based feedback (MFB) approach [10] assumes feedback documents related to a query q are generated through a two-component mixture model, of which one component is the background topic θC and the other component is a query dependent topic θq .

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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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