Discovering Good Sources for Recommender Systems

摘要

Discovering user knowledge is a key issue in recommender systems and many algo-rithms and techniques have been used in the attempt. One of the most critical problems in re-commender 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 informationabout users [1], but they do not solve the new problem that arises: which relevant database touse? This paper contributes to that solution with a novel method for selecting informationsources in the belief that they will be relevant and will result in better recommendations. Wedescribe a new approach to explore and discover relevant information sources in order to obtainreliable knowledge about users. The relation between the improvement of the recommendationresults and the sources selected based on these characteristics is shown by experiments select-ing source based on their relevance and trustworthiness.