Advances in Information Retrieval Theory: Third by ChengXiang Zhai (auth.), Giambattista Amati, Fabio Crestani

By ChengXiang Zhai (auth.), Giambattista Amati, Fabio Crestani (eds.)

This e-book constitutes the refereed court cases of the 3rd overseas convention at the idea of data Retrieval, ICTIR 2011, held in Bertinoro, Italy, in September 2011. The 25 revised complete papers and thirteen brief papers offered including the abstracts of 2 invited talks have been rigorously reviewed and chosen from sixty five submissions. The papers disguise issues starting from question growth, co-occurence research, person and interactive modelling, procedure functionality prediction and comparability, and probabilistic ways for score and modelling IR to subject matters concerning interdisciplinary methods or purposes. they're geared up into the subsequent topical sections: predicting question functionality; latent semantic research and observe co-occurrence research; question enlargement and re-ranking; comparability of data retrieval structures and approximate seek; chance score precept and possible choices; interdisciplinary methods; person and relevance; outcome 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

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This representation is used [k] [k] to re-rank the given result list LM . The resultant re-ranked version of LM is [k] presumably of relatively high quality, and is thereby denoted here LP E . The [k] [k] [k] [k] similarity between LM and LP E (Sim(LM , LP E )) is measured using Kendall’sτ , Pearson’s coefficient, or Spearman’s-ρ. The similarity value is scaled by an estimate for the quality of the information need representation. The motivation is to model the confidence in the ability to derive an effective representation of the information need, and use the level of confidence so as to adjust the prediction value.

We consider three natural random variables in this space: the user, the item, and the rating value, involved in a rating assignment by a user to an item. This gives meaning to the distributions expressed in the different versions of clarity as defined in the previous | represents the probability that a specific item is rated section. For instance, with a value –by a random user–, is the probability that an item is rated –with any value by any user–, and so on. The probability distributions upon which the proposed clarity models are defined can use different estimation approaches, depending on the independence assumptions and the amount of involved information.

Org/node/73 36 A. Bellogín, P. Castells, and I. Cantador UB, and item-based, as IB) as implemented in the Mahout library2. We used two additional algorithms, recently developed, which obtain very good performance in terms of precision metrics, which we denote as TF-L1 and TF-L2 [4]. They implement an item-based CF approach with different normalization and weighting functions for the similarity or rating values. Finally, we implemented a content-based recommender (denoted as CBF) using movie genre, director, and country, from IMDb3, as item attributes.

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