With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, collaborative filtering (CF)-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

中文翻译：

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推荐技术及其在医疗保健中的应用概述

随着互联网上信息量的增加，推荐系统（RS）已在各种领域中用作克服信息过载的有效工具。近年来，由于RS在提供适当建议和帮助人们做出有关其健康的正确决策方面的巨大优势，RS在健康方面的应用已成为一个日益增长的研究主题。本文旨在对典型推荐技术及其在医疗保健领域的应用进行全面综述。更具体地，概述了三种著名的推荐技术，即基于内容，基于协作过滤（CF）的方法和混合方法。接下来，我们提供有关健康RS的五个应用场景的快照，它们是饮食推荐，生活方式推荐，培训建议，针对患者和医生的决策以及与疾病相关的预测。最后，提出了一些关键挑战，并为这一新的蓬勃发展的领域提供了明确的理由。