Hierarchical Physician Recommendation via Diversity-enhanced Matrix Factorization

Recent studies have shown that there exhibits significantly imbalanced medical resource allocation across public hospitals. Patients, regardless of their diseases, tend to choose hospitals and physicians with a better reputation, which often overloads major hospitals while leaving others underutilized. Guiding patients to hospitals that can serve their treatment needs both timely and with good quality can make the best use of precious medical resources. Unfortunately, it remains one of the major challenges both for research and in practice. In this article, we propose a novel diversity-enhanced hierarchical physician recommendation approach to address this issue. We adopt matrix factorization to estimate physician competency and exploit implicit similarity relationships to improve the competency estimation of physicians that we are of little information of. We then balance the patient preference and physician diversity using two novel heuristic algorithms. We evaluate our proposed approach and compare it with the state of the art. Experiments show that our approach significantly improves both accuracy and recommendation diversity over existing approaches.