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.

中文翻译：

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通过多样性增强矩阵分解的分级医师推荐

最近的研究表明，公立医院之间的医疗资源分配明显不平衡。患者，无论他们的疾病如何，都倾向于选择声誉较好的医院和医生，这往往会使主要医院超负荷运转，而其他医院却未得到充分利用。将患者引导到能够及时、优质地满足患者就诊需求的医院，可以充分利用宝贵的医疗资源。不幸的是，它仍然是研究和实践中的主要挑战之一。在本文中，我们提出了一种新的多样性增强的分层医生推荐方法来解决这个问题。我们采用矩阵分解来估计医生的能力，并利用隐含的相似关系来改进我们缺乏信息的医生的能力估计。然后，我们使用两种新颖的启发式算法来平衡患者偏好和医生多样性。我们评估我们提出的方法并将其与最先进的技术进行比较。实验表明，与现有方法相比，我们的方法显着提高了准确性和推荐多样性。