Research On Fruit Recommendation Methods For Different Nutrient Needs Of People

As an important component of the dietary structure,fruits provide a variety of nutrients for the human body,and different populations have different needs for these nutrients.How to recommend fruits that meet the nutritional needs of different populations has become a problem.Traditional recommendations only consider fruits as ordinary commodities,ignoring users’ demand for fruit nutrients.This article focuses on this issue and conducts research on recommended methods for nutritional needs of different populations:1.For the fitness population,based on the consumption of trace elements during the fitness process,the current situation of minerals,vitamins,and dietary fiber deficiency in their dietary structure was analyzed.Based on the standard values of nutrient intake in fruits and vegetables,factors such as the "Chinese Food Composition Table" and the Food Blood Sugar Generation Index(GI)were compared and studied.The nutritional components of 10 most popular fruits in China were established as an analysis dataset,Using the Delphi Analytic Hierarchy Process to study and determine the indicators and weights for judging recommended fruits,and then using the yahhp software for data analysis,based on the overall score,a recommended plan for fruit nutrition needs of different fitness groups for muscle gain and weight loss was proposed;2.For different age groups,the daily total nutritional requirements were obtained based on the "Dietary Nutrient Intake of Chinese Residents" standard.The main nutritional components and total intake from fruits were analyzed,and the optimization goals for the daily nutritional components and total intake from fruits were established for different age groups.Select 23 types of fruits through the nutritional composition network to construct a fruit nutritional composition dataset.The particle swarm optimization algorithm was used to optimize the main nutritional components and total amount obtained from fruits for different age groups,and fruit recommendation plans and their total nutritional value for each age group were constructed.After calculation,fruit recommendation plans for each age group were given;3.For people with different preferences,useful information in the fruit dataset was analyzed based on the historical information of different groups purchasing fruits.Recommendation algorithms were used to model the fruit.As the fruit dataset only contains three useful information: user,fruit name,and fruit rating,this chapter fused item and rating information,and used penalty factors as weights for users purchasing the same fruit,Then weighted with cosine similarity,and finally evaluated the hybrid model using evaluation indicators such as recall,accuracy,and F1 score,ultimately verifying the feasibility of the hybrid model;Based on the above three different population classifications,different research methods and algorithms were adopted,and traditional recommendation algorithms were used to analyze and compare different similarities.The optimal recommendation model was selected through evaluation indicators such as recall rate,accuracy,and F1 score.Combined with the nutritional needs of fitness or different age groups,a comprehensive recommendation plan was proposed.