Approach to Cold-Start Problem in Recommender Systems in the Context of Web-Based Education

Abstract

In this paper we present an approach to treatment of the Cold-Start Problem in Recommendation System for Environment Education Web. Our approach is based on the concept of Coupled-Learning and Bootstrapping. Based on an initial set of data we apply algorithms traditional machine learning to cooperate with each other, forming various views on its outputs and allowing the data set to be classified incrementally. Thus, it is possible to increase the initial volume of data and to improve the performance of a recommender more instances for analysis. The vast majority of the efforts attack the cold start problem with variations of the CBF algorithm. In our approach, we use the incremental semi-supervised learning based on pairs in order to increase the initial training set in order to allow the generation of more recommendations.