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

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

ISBN-10: 364223318X

ISBN-13: 9783642233180

This publication constitutes the refereed court cases of the 3rd foreign convention at the thought of knowledge 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 hide issues starting from question growth, co-occurence research, consumer and interactive modelling, process functionality prediction and comparability, and probabilistic ways for rating and modelling IR to issues concerning interdisciplinary ways or purposes. they're prepared into the next topical sections: predicting question functionality; latent semantic research and notice co-occurrence research; question growth and re-ranking; comparability of data retrieval platforms and approximate seek; likelihood score precept and choices; interdisciplinary techniques; consumer and relevance; consequence diversification and question disambiguation; and logical operators and descriptive approaches.

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Extra resources for Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings

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3 is approximated using an estimate for the [k] quality of the result list LM , which is in turn estimated based on the similarity [k] of LM with the result lists of the PE and PIE rankings: ˆ P E;P IE (πM (q; D)) ≈ α(q)Sim(L[k] , L[k] ) − β(q)Sim(L[k] , L[k] ) . Q M PE M P IE (4) Various inter-ranking (list) similarity measures (Sim(·, ·)) can be used. For example, if both lists are (different) rankings of the same document set, then Kendall’s-τ , which uses rank information, or Pearson’s correlation coefficient computed based on retrieval scores in the lists, can be applied.

Among them, we focus on a subset that is shown in Table 2, which provide the most interesting combinations, in terms of experimental efficiency, of user and background distributions for each clarity model. These alternatives are further analyzed in detail in the next sections. Table 2. Different user clarity models implemented User clarity name RatUser RatItem ItemSimple ItemUser IRUser IRItem IRUserItem User dependent model |, ; | |, ; | | | |, |, |, Background model | | | Predicting the Performance of Recommender Systems 33 5 Qualitative Observation In order to illustrate the proposed prediction framework and give an intuitive idea of what the user characteristics predictors are capturing, we show the relevant aspects of specific users that result in clearly different predictor values, in a similar way to the examples provided in [6] for query clarity.

2 that NQC’s prediction quality monotonically improves when increasing k up till a point from which it monotonically decreases. Indeed, with very few documents in the result list (small k), the centroid is much affected by highly ranked relevant documents; thereby, it is not a very good basis for a useful ineffective reference list. Having more documents in the list when increasing k towards its optimal value, results in considering more non-relevant query-similar documents; thus, the centroid’s usefulness for constructing ineffective reference ranking grows, and accordingly, prediction is improved.

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