By ChengXiang Zhai (auth.), Giambattista Amati, Fabio Crestani (eds.)
This ebook constitutes the refereed court cases of the 3rd overseas convention at the thought of data Retrieval, ICTIR 2011, held in Bertinoro, Italy, in September 2011. The 25 revised complete papers and thirteen brief papers provided including the abstracts of 2 invited talks have been conscientiously reviewed and chosen from sixty five submissions. The papers conceal themes starting from question growth, co-occurence research, consumer and interactive modelling, approach functionality prediction and comparability, and probabilistic techniques for score and modelling IR to issues on the topic of interdisciplinary ways or purposes. they're equipped into the next topical sections: predicting question functionality; latent semantic research and be aware co-occurrence research; question enlargement and re-ranking; comparability of knowledge retrieval structures and approximate seek; likelihood score precept and possible choices; interdisciplinary techniques; person and relevance; outcome diversification and question disambiguation; and logical operators and descriptive approaches.
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Additional resources for Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings
Example text
We believe the difference in correlation for CBF and IB recommenders may be explained considering two factors: the actual recommender performance, and the input sources used by the recommender. With regards to the first factor, the IB algorithm performs poorly (in terms of the considered ranking quality metrics, such as nDCG and MAP) in comparison to the rest of recommenders. It seems natural that a good predictor for a well performing algorithm (specifically, TF-L2 is the best performing recommender in this context) would hardly correlate at the same time with a poorly performing one.
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.
In the following, we define different user performance predictors, whose main goal is to infer how good or bad the system is expected to perform for a given user. We propose a fairly general adaptation of query clarity, which may be instantiated in different models, depending on the input spaces considered. Specifically, our adaptation of query clarity has the following formulation: clarity | log | (1) 30 A. Bellogín, P. Castells, and I. Cantador As we can observe, the clarity formulation strongly depends on a “vocabulary” space , which further constrains the user-conditioned model (or user model for short) | , and the background probability .