Um estudo comparativo de técnicas para a classificação contextual de companhia para sistemas de recomendação sensíveis a contexto

Abstract

Nowadays, the vast amount of information has harmed users during decision making. In face of this problem, recommendation systems have been proposed in order to offer suggestions that help users to overcome such problem. These suggestions are even more valuable when these systems begin to suggest items based on the user contexts. Among these contexts, the companion context can be highlighted. Through the inference of the companion context the system may suggest different items if the user is accompanied or not. An example of a system that has such features is the CD-CARS. However, the unsupervised learning method for companion inference on CD-CARS has some limitations. In this way, the present research analyzed and highlighted a supervised learning method that can replace the current company contextual classification approach executed in the CD-CARS.