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Due to the vast amount of publicly available online data, people may find it difficult to obtain relevant information to find food or meals that match their taste and health while maintaining a healthy lifestyle. The overload of information makes it difficult to separate relevant, personalized information from massive volumes of data. Recommendation systems (RS) are suggestion systems that provide users with information that they may be interested in. With RS, this enormous amount of information is filtered and analyzed for further insights. This paper will explore several generations of recommender systems in the healthcare industry. This paper thoroughly analyses the current state-of-the-art recommender systems focusing on the grouping, methods, application and evaluation metrics. In addition, several challenges for further research and improvement in this domain are also outlined in the paper. [ABSTRACT FROM AUTHOR]