Web Information Systems Engineering – WISE 2014: 15th by Boualem Benatallah, Azer Bestavros, Yannis Manolopoulos,

By Boualem Benatallah, Azer Bestavros, Yannis Manolopoulos, Athena Vakali, Yanchun Zhang

This booklet constitutes the lawsuits of the fifteenth foreign convention on internet info structures Engineering, clever 2014, held in Thessaloniki, Greece, in October 2014.
The fifty two complete papers, sixteen brief and 14 poster papers, provided within the two-volume complaints LNCS 8786 and 8787 have been rigorously reviewed and chosen from 196 submissions. they're geared up in topical sections named: internet mining, modeling and class; net querying and looking out; net advice and personalization; semantic internet; social on-line networks; software program architectures amd systems; internet applied sciences and frameworks; net innovation and purposes; and challenge.

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Additional resources for Web Information Systems Engineering – WISE 2014: 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, Proceedings, Part I

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Di Pietro, M. Petrocchi, and A. Spognardi n n where [ 2j ] is the greatest integer less than or equal to 2j . In practice, if the sample size is an odd number, the median is defined to be the middle value of the ordered samples; if the sample size is even, the median is the average of the two middle values [24]. , the mean obtained discarding a percentage of the lowest and highest values. Formally, the 100α% trimmed average μα of our nj ratings is obtained ordering the values and evaluating: x[nj α]+1 + · · · + xnj −[nj α] μα = nj − 2[nj α] where [nj α] is the greatest integer less than or equal to nj α.

Com. We collect all the scores assigned to these hotels, for a total of about one million of scores. We freshly introduce here two new metrics, the “slotted” mean and “slotted” median, and, based on our score-set, we compare the rankings based on the new metrics with the ones based on the mean and the median. com). , [13,19,12]. , weeks or months) and, then, calculating, respectively, the mean and median of the averages over the slots. All the aggregators considered in this paper (mean, median, and slotted aggregators) are compared with respect to different properties, such as the dispersion of the reviewers’ scores around each aggregator, the similarity of the rankings obtained sorting the hotels per aggregator, and the robustness of each aggregator to resist to injection of outliers (in terms of degree of ranking alteration).

The learning procedure is constrained two-fold: the learned rating values should be as close as possible to the observed rating values, and the predicted item profiles should be similar to their neighbourhoods as well, which are derived from their implicit coupling information. More specifically, item coupling is incorporated by adding an additional regularization factor in the optimization step. Then, the computation of the mapping can be similarly optimized by minimizing the regularized squared error.

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