@Article{cmc.2020.09981,
AUTHOR = {Hongtao Bai, Xuan Li, Lili He, Longhai Jin, Chong Wang, Yu Jiang},
TITLE = {Recommendation Algorithm Based on Probabilistic Matrix Factorization with Adaboost},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1591--1603},
URL = {http://www.techscience.com/cmc/v65n2/39895},
ISSN = {1546-2226},
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 probabilistic matrix factorization method extracts the implicit factors
between individual food preferences and nutritional characteristics. From this, we can 
make use of those features with strong influence while discarding those with little 
influence. After incorporating these changes into our approach, we evaluated our 
algorithm’s performance. Our results show that our method performed better than others 
at matching preferred foods with dietary requirements, benefiting user health as a result. 
The algorithm fully considers the constraint relationship between users’ attributes and 
nutritional characteristics of foods. Considering many complex factors in our algorithm, 
the recommended food result set meets both health standards and users’ dietary 
preferences. A comparison of our algorithm with others demonstrated that our method 
offers high accuracy and interpretability.},
DOI = {10.32604/cmc.2020.09981}
}