By ChengXiang Zhai (auth.), Giambattista Amati, Fabio Crestani (eds.)
This publication constitutes the refereed court cases of the 3rd overseas convention at the concept of data Retrieval, ICTIR 2011, held in Bertinoro, Italy, in September 2011. The 25 revised complete papers and thirteen brief papers awarded including the abstracts of 2 invited talks have been conscientiously reviewed and chosen from sixty five submissions. The papers conceal themes starting from question enlargement, co-occurence research, consumer and interactive modelling, process functionality prediction and comparability, and probabilistic ways for score and modelling IR to themes on the topic of interdisciplinary ways or functions. they're equipped into the next topical sections: predicting question functionality; latent semantic research and observe co-occurrence research; question enlargement and re-ranking; comparability of knowledge retrieval structures and approximate seek; chance score precept and choices; interdisciplinary methods; consumer and relevance; consequence diversification and question disambiguation; and logical operators and descriptive approaches.
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Extra info for Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings
On Knowl. and Data Eng. 17(6), 734–749 (2005) 2. : Query difficulty, robustness, and selective application of query expansion. I. ) ECIR 2004. LNCS, vol. 2997, pp. 127–137. Springer, Heidelberg (2004) 3. : A performance prediction approach to enhance collaborative filtering performance. , van Rijsbergen, K. ) ECIR 2010. LNCS, vol. 5993, pp. 382–393. Springer, Heidelberg (2010) 4. : Text retrieval methods for item ranking in collaborative filtering. , Mudoch, V. ) ECIR 2011. LNCS, vol. 6611, pp.
Alternatively, fusion of multiple rankings can be used to produce a pseudo eﬀective ranking . Indeed, the merits of fusion, in terms of retrieval eﬀectiveness, have been acknowledged . Pearson’s correlation between the given result list and that produced by fusion served for query-performance prediction . Clearly, this prediction approach is a speciﬁc instance of our framework (with α(q) = 1 and β(q) = 0). Intermediate summary. As was shown above, various post-retrieval predictors can be derived from Eq.
Furthermore, if we compare the background model with the user model, we obtain more insights about how our models are discriminating distinctive from mainstream behavior. This is depicted in Fig. 2. In this situation, we select those terms which maximize the difference between the user and background models. Then, for this subset of the terms, we sort the vocabulary with respect to its collection probability, and then we plot the user probability model for each of the terms in the vocabulary. These figures show how the most ambiguous user obtains a similar distribution to that of the background model, while the distribution of the less ambiguous user is more different.
Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings by ChengXiang Zhai (auth.), Giambattista Amati, Fabio Crestani (eds.)