Discovering Good Sources for Recommender Systems

Discovering user knowledge is a key issue in recommender systems and many algorithms and techniques have been used in the attempt. One of the most critical problems in recommender systems is the lack of information, referred to as Cold Start and Sparsity problems. Research works have shown how to take advantage of additional databases with information about users [1], but they do not solve the new problem that arises: which relevant database to use? This paper contributes to that solution with a novel method for selecting information sources in the belief that they will be relevant and will result in better recommendations. We describe a new approach to explore and discover relevant information sources in order to obtain reliable knowledge about users. The relation between the improvement of the recommendation results and the sources selected based on these characteristics is shown by experiments selecting source based on their relevance and trustworthiness.