By Gilbert Babin, Peter Kropf, Michael Weiss
This quantity constitutes the court cases of the 4th overseas convention on E-Technologies, MCETECH 2009, held in Ottawa, Canada, in the course of may perhaps 4-6, 2009. The 23 complete and 4 short papers included during this quantity have been rigorously reviewed and chosen from a complete of forty two submissions. They hide issues corresponding to inter-organzational techniques, service-oriented architectures, defense and belief, middleware infrastructures, open resource and open environments, and purposes together with eGovernment, eEducation, and eHealth.
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Extra resources for E-Technologies: Innovation in an Open World: 4th International Conference, MCETECH 2009, Ottawa, Canada, May 4-6, 2009, Proceedings (Lecture Notes in Business Information Processing)
Example text
The similarity between the researcher a and his neighbor u is derived using Pearson correlation coefficient highlighted in the following equation: (1) 32 A. Naak, H. Hage, and E. Aϊmeur Where ra,i is the rating given by researcher a to article i, ra is the mean rating given by researcher a, and m is the total number of articles. The next step is to use this neighborhood to predict the researcher’s rating to an unrated article. Such predictions are computed as the weighted average of deviations from the neighbor’s mean using the following equation: (2) Where pai is the rating prediction of researcher a for resource i, sima,u is the similarity between researchers a and u obtained using equation (1), and k is the number of researchers in the neighborhood.
Moreover, we propose, test and compare several approaches to determine the neighbourhood in the Collaborative filtering process such as to increase the accuracy of the recommendation. Keywords: Multi-criteria recommendation, collaborative filtering, research paper recommendation, eEducation. 1 Introduction In any research field, graduate students and professors (henceforth referred to as researchers) regularly access, read and keep research papers of interest. With the advent of digital libraries, such as the ACM Digital Library1, IEEExplore2 and SpringerLink3, most of these research papers can be accessed online and stored on personal computers in an electronic format.
In other words, for each criteria j, the k users with the highest similarity in rating the criteria j are used in the prediction. The rationale behind this approach is to use the closest neighbors for each criteria instead the closest neighbors overall. As such, this approach takes advantage of the multi-criteria setting, while maximizing neighborhood’s similarity with the target researcher for each criterion separately. Yet this approach suffers greatly when the neighborhood is not very close to the target user.