The development of personalized wellness therapy recommender system using hybrid case-based reasoning

Rising costs in health care have encouraged individuals to become more aware of disease prevention. Therapy in the prevention of disease is very popular; this complementary and alternative medicine treatment is known as “wellness”. Wellness therapy is about balancing the harmony of body, mind and spirit, through physical beauty care, healthy diet, relaxation, environmental sensitivity and social contacts. However, in today's digital world, wellness information is not sufficient to meet the demands of users who tend to seek for wellness therapies suitable for themselves. Therefore, the wellness industry should provide effective solutions customised to an individual's needs and preferences. A study on enhancing the wellness recommendation services available on a wellness community portal was carried out to find the best technique(s) for matching users' wellness concerns with appropriate wellness therapy, and making reliable wellness recommendations based on individual preferences and wellness conditions. Hybrid case-based reasoning (HCBR) is the best approach for wellness therapy recommendation. HCBR uses three alternatives in suggesting solutions to users. The first alternative is to locate the same case in a case database to solve a user's current wellness concern. The second alternative is to match a user's wellness concerns with existing similar cases in a case database. The third alternative is to use a set of pre-determined standard rules in a rule-based reasoning (RBR) sub-system. The model proposed below will enhance users' knowledge and convenience in selecting suitable wellness therapy for their complementary and alternative medicine treatments.