Download Advances in Information Retrieval: 31th European Conference by W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, PDF

By W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.)

This publication constitutes the refereed lawsuits 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 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, review, opinion detection, net IR, illustration, clustering / categorization in addition to allotted IR.

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Extra resources for Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings

Example text

If the top-ranked document’s probability of relevance has a large associated variance, then there is a much greater likelihood that we have significantly under or over estimated the probability of relevance of the document. If we underestimate the probability of relevance, then the user will likely be pleased with the choice. If we overestimate the probability of relevance, then the user will likely be displeased with our choice. If we average the user’s perceived relevance over many queries, choosing top-ranked documents that have high variance will result in a user experiencing a high degree of variability in the search results.

The covariance matrix of documents is calculated by users’ ratings. org/taxonomy/term/14), and adopted a subset described in [8], which contains 2,000 users. Each user has rated as least 40 items. The rating scale is indicated as a value between 1 and 6, with 1 being the lowest rating and 6 being the highest one. In our study, only the rating values 5 and 6 were regarded as relevant. For testing, we assigned the users in the data set randomly to a training user and a test user set. Users in the training set were used as the basis for making predictions, while our test users were considered as the ground truth for measuring prediction accuracy.

To address the uncertainty, the final ranking is equal to the mean of the normal distribution subtracted (or added) by a weighted variance. A positive b produces a risk-averse (conservative) ranking where the unreliably-estimated documents (with big variance) should be given a lower ranking score. The bigger the parameter b is, the more conservative the ranker is. On the other hand, a negative b gives the risk-inclined ranking. Substituting the estimated mean in Eq. (2) and variance in Eq. (3) into Eq.

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