  Recommender systems have fulfilled an important role in everyday life.
Recommendations such as news by Google, videos by Netflix, goods by e-commerce
providers, etc. have heavily changed everyones lifestyle. Health domains
contain similar decision-making problems such as what to eat, how to exercise,
and what is the proper medicine for a patient. Recently, studies focused on
recommender systems to solve health problems have attracted attention. In this
paper, we review aspects of health recommender systems including interests,
methods, evaluation, future challenges and trend issues. We find that 1) health
recommender systems have their own health concern limitations that cause them
to focus on less-risky recommendations such as diet recommendation; 2)
traditional recommender methods such as content-based and collaborative
filtering methods can hardly handle health constraints, but knowledge-based
methods function more than ever; 3) evaluating a health recommendation is more
complicated than evaluating a commercial one because multiple dimensions in
addition to accuracy should be considered. Recommender systems can function
well in the health domain after the solution of several key problems. Our work
is a systematic review of health recommender system studies, we show current
conditions and future directions. It is believed that this review will help
domain researchers and promote health recommender systems to the next step.
