Using Support Vector Machines for feature-oriented profile-based recommendations. (26th June 2009) - Garcia-Crespo, Angel; Gomez-Berbis, Juan Miguel; Colomo-Palacios, Ricardo; Garcia-Sanchez, Francisco
Recommendations have reached a new dimension in a ubiquitous computing environment. A vast amount of information is coming from pervasive services and devices, providing a potential source of value-added knowledge. However, this knowledge must be exposed through a classification technique such as Support Vector Machines (SVMs), which would allow categorising, classifying and evaluating this information based on features and profiles to foster the efficiency of recommendations of informational items. This paper presents an algorithm based on SVMs and an underlying architecture to extract and build a number of recommendations based on features and profile preferences from the user.