In this paper, we use the O*NET-SOC taxonomy, a taxonomy that defines the set of occupations across the world of work, to develop a new taxonomy-based vector model for social network users and job descriptions suited to the task of job recommendation we propose two similarity functions based on the AND and OR fuzzy logic's operators, suited to the proposed vector model.
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This paper presents taxonomy-based recommender systems that propose relevant jobs to Facebook and LinkedIn users they are being developed by Work4, a San Francisco-based software company and the Global Leader in Social and Mobile Recruiting that offers Facebook recruitment solutions to use its applications, Facebook or LinkedIn users explicitly grant access to some parts of their data, and they are presented with the jobs whose descriptions are matching their profiles the most. The experimental results indicate an accuracy value of 92.55% which is very promising. The three different kernels tested allow a significant improvement of the classification performances and a flexibility to balance between the spatial and spectral information in the classifier. The proposed approach was tested on common scenes of urban imagery. For this purpose we propose a methodology exploiting the properties of Mercer's kernels to construct a family of composite kernels that easily combine multi-spectral features and Haralick texture features as data source. A number of works have shown promising results by the fusion of spatial and spectral information using Support vector machines (SVM) which are a group of supervised classification algorithms that have been recently used in the remote sensing field. The classification of remotely sensed images knows a large progress taking in consideration the availability of images with different resolutions as well as the abundance of classification's algorithms.