Hongtao Bai1, 2, Xuan Li1, 2, Lili He1, 2, Longhai Jin1, 2, Chong Wang1, 2, 3, Yu Jiang1, 2, *
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1591-1603, 2020, DOI:10.32604/cmc.2020.09981
Abstract A current problem in diet recommendation systems is the matching of food
preferences with nutritional requirements, taking into account individual characteristics,
such as body weight with individual health conditions, such as diabetes. Current dietary
recommendations employ association rules, content-based collaborative filtering, and
constraint-based methods, which have several limitations. These limitations are due to the
existence of a special user group and an imbalance of non-simple attributes. Making use
of traditional dietary recommendation algorithm researches, we combine the Adaboost
classifier with probabilistic matrix factorization. We present a personalized diet
recommendation algorithm by taking advantage of probabilistic matrix factorization via
Adaboost. A… More >
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