Open AccessPersonalized Diet Recommendation Based on K-means and Collaborative Filtering Algorithm
Author(s) -
Zhicai Yuan,
Fei Luo
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1213/3/032013
Subject(s) - collaborative filtering , disjoint sets , computer science , cluster analysis , set (abstract data type) , balance (ability) , k means clustering , recommender system , algorithm , healthy diet , data mining , information retrieval , machine learning , medicine , mathematics , food science , combinatorics , physical medicine and rehabilitation , programming language , chemistry
With the improvement of people’s living standards, people pay more and more attention to the health of diet, and traditional dietary recommendations are difficult to meet the user’s dietary preferences and nutritional balance. This paper first uses the k-means clustering algorithm to divide the food set into multiple disjoint subsets, then uses the user-based collaborative filtering algorithm to recommend the food that the user may like. The recommended food and food in standard recipes set by user’s own situation are in the same cluster, which meets the user’s nutritional balance. The experimental results show that the recommended effect of this method is effective, and the recommended accuracy rate is over 70%.