Content-bootstrapped Collaborative Filtering for Medical Article Recommendations

Abstract

Recommender system seeks to assist and augment the natural social process of making choices without sufficient personal experience of the alternatives. They have become fundamental applications in electronic commerce and information access, assisting users to effectively pinpoint information that of their interests from large catalog spaces. Contrary to the pervasive utilization of recommender systems in domains such as electronic commerce, the application of recommendation system in medical domain is limited and further effort is needed. In addition, while a variety of approaches have been proposed for performing recommendation, including collaborative filtering, demographic recommender and other techniques, each individual method has its own drawbacks. This paper proposes a medical oriented recommendation system in which patient’s background data is used to bootstrap the collaborative filtering engine and personalized suggestions are provided therein. We present empirical experiment results that show how the content-bootstrapped part of the system enhances the effectiveness of medical article recommendation of the collaborative filtering.