Online physician recommender systems alleviate information overload by automatically recommending the best-fit physicians to patients. In contrast to general recommendations, physicians with greater uncertainty (i.e., greater variance in patients’ feedback) may not be preferred as this could affect patients’ treatment. However, most existing recommender systems don't consider uncertainty, reducing systems’ reliability and patients’ readiness to trust. To address this concern, this study leverages Bayesian theory and develops an uncertainty-aware online physician recommender system, including a Bayesian deep collaborative filtering (BDCF) model and a novel uncertainty-aware ranking algorithm. Experiments on real-world data demonstrate the superiority of BDCF and the ranking algorithm.

中文翻译：

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用于不确定性在线医生推荐的贝叶斯深度推荐系统


在线医生推荐系统通过自动向患者推荐最合适的医生来减轻信息过载。与一般建议相反，具有较大不确定性（即患者反馈差异较大）的医生可能不是首选，因为这可能会影响患者的治疗。然而，大多数现有的推荐系统没有考虑不确定性，从而降低了系统的可靠性和患者的信任意愿。为了解决这个问题，本研究利用贝叶斯理论并开发了一种不确定性感知的在线医生推荐系统，包括贝叶斯深度协作过滤（BDCF）模型和一种新颖的不确定性感知排名算法。对真实世界数据的实验证明了BDCF和排序算法的优越性。