# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 5 Business Adoption and Applications
### Recommendation Systems in the e-Health Domain  
E-health and medical decisions are considered for RSs' research, aiming to help medical professionals take fast and proper medical decisions. In [6], researchers proposed a RS to recommend medical advice for cardiovascular disease patients. They tackled the problems in traditional collaborative RSs such as scalability and sparsity, and they developed a new technique by applying clustering and sub-clustering methods. The authors used k-means for clustering, and they evaluated their model using precision, recall, and MAE (Mean Absolute Error). In [65] Weighted hybrid recommendation filtering approach was adopted along with an autonomic evaluation of patients' needs to propose personalized services tackling the patients' mental health. They used the self-questionnaires method to form users' profiles. In [66], an order RS for clinics was proposed. The system predicts the order of contents for each appointment and provides recommendations for providers who like to place the order. The authors used data from five outpatient clinics, and they aimed to enhance order effectiveness. The researchers also used medical heterogeneous records and data sources in [67] to develop a RS that recommends standard treatment plans for given symptoms. In [68], the authors considered the increase in personal data acquisition and mobile health systems. They developed a fuzzy optimization model to enhance a mobile wellness recommender by incorporating various imprecision levels such as fuzzy, crisp, interval-valued fuzzy parameters. They evaluated their proposed model using accuracy, specificity, and sensitivity (i.e., true negative rate). The authors in [69] developed an e-health collaborative-based RS using deep learning. They applied CNN (convolutional neural network) algorithm, and they evaluated their recommender using precision, recall, MAE, and RMSE values. The authors used sentiment analysis to obtain patients' opinions, and they kept the patients' privacy preserved. The authors in [70] developed a tailored hybrid RS incorporating demographic, utility-based, and content-based filtering techniques. They aimed to help smokers quit smoking by sending them motivational messages seeking to change their behavior. They evaluated their model using the F-measure, MAE, and the hit rate. The authors in [71] tackled the patients' dietary needs by proposing a novel method to recommend food, based on the patients' medical history. They also included other features such as age, weight, gender, calories, protein, and fat. They combined deep learning and machine learning methods--such as naive Bayes, recurrent neural network, and Long Short Term-Memory (LSTM)--and applied them on a 30 patients' collected dataset. The emotional health-related RS is also proposed in [72]. The authors used a knowledge-based filtering recommender to suggest remedies for the patients, such as music therapy, art therapy, naturopathy, etc. that can enhance their given state of health. In [73], the authors used a hybrid filtering approach, combining context-based with collaborative filtering techniques to discover a cohort of rare diseases. They applied the model to an Alzheimer's disease dataset.