# Implementation of Key Technologies for a Healthy Food Culture Recommendation System Using Internet of Things

Authors: Weiguang Xie; Hongliang Lou
Journal: Mobile Information Systems
 (2022)

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## Abstract

With the development of catering culture, the types of diets are changing with each passing day. The types of food are becoming more and more abundant, and the concept of healthy eating has become more and more prominent in people’s thinking. The development of Internet of Things technology enables people to live a variety of information in the network, and the amount of information increases sharply. With the development of the Internet of Things, the number of online recipes has increased significantly, and a number of choices are now available to people to find suitable recipes. It is still troublesome for people to select the menu of three meals on daily basis. In recent years, many people have long-term unhealthy eating habits, which has greatly increased the incidence of some diseases. Therefore, a balanced diet is very important for good health. This paper builds a fusion of healthy eating culture with the help of Internet of Things, deep learning, and other related technologies. A more practical healthy diet recommendation system is built, which aims to help people find diet recipes that conform to their personal eating preferences and habits and their health status. Experimental results show that the healthy food culture recommendation system constructed in this paper can more accurately recommend suitable healthy food recipes for individuals and promote people’s health. And the launched recipes can ensure nutrition and meet individual taste preferences, which has great practical significance.


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## Body

## 1. Introduction

The emergence of the Internet has brought a lot of convenience to all aspects of people’s lives, and it has become the norm for people to obtain information from the Internet, including recipe information [1]. But in this era of information explosion, it will undoubtedly take a lot of time and energy for people to get the healthy recipe information they really want from hundreds of decentralized information. For example, food information is undoubtedly a needle in a haystack. Therefore, in this fast-paced era, the recommendation system was born. The recommender system can predict individual preferences according to the interaction between individuals and the system, and recommend the required information to individuals, which can save people’s time to query information and greatly improve the efficiency of information acquisition. Although recommendation systems are used in many fields, after investigation and analysis, there are few studies on recommendation systems for healthy recipes. At present, most people are in a sub-health state, and the pursuit of a healthy lifestyle is becoming more and more normal. Therefore, it is particularly important to build a healthy recipe that can be recommended to promote personal health according to people’s personal preferences and health status.This paper provides a healthy diet recommendation system that is more in line with the user’s own preferences. It integrates personalized recommendation into the recommendation system, changing the problem that the traditional recipe recommendation system cannot recommend recipes according to personal eating habits and preferences and personal physical conditions [2]. This paper builds a healthy recipe recommendation system for individual needs, which is of practical significance for popularizing the knowledge of sports nutrition, improving people’s correct understanding of the role of diet on human health, helping people to rationally formulate a reasonable healthy eating plan, and improving people’s physical quality. The constructed healthy diet recommendation system can take into account the user’s actual nutritional needs and the user’s taste preference needs, so as to achieve a balance between nutrition and taste. This recommendation method that can satisfy users’ taste preferences can not only help healthy users prevent the occurrence of various diseases and maintain a strong body, but also improve people’s current health status. The reconstructed recommendation system for healthy food culture can provide guidance for the construction of various recommendation systems in the future.People take food as their heaven, and the research on healthy diet has always been a research hotspot. Many scholars have done a lot of specific research on how to improve the health of diet. Among them, Somaye et al. develop a food recommendation system that combines artificial intelligence technology. He also developed a more comprehensive dietary recommendation system for diabetes, including main meals, based on a knowledge base based on guidelines proposed by the American Diabetes Association. Experiments have shown that patients are satisfied with the use of the system, which is believed to help diabetic patients adopt a healthier diet [3]. Heard and Miller simulate the introduction of an integrated refrigerated supply chain or cold chain into sub-Saharan Africa. He proposed both reducing food loss and its associated environmental impact while increasing energy use and generating greenhouse gas emissions. It is estimated that the increased postharvest emissions from cold chain operations are greater than the avoided food loss emissions [4]. Ha et al. developed a program for each patient to prescribe allergy formulas, delivering standards- and guideline-based food allergy management for safe allergen meals. The utility of the Introduced Food Allergy Program was then assessed by comparing the number of allergy prescriptions of the Introduced Food Allergy Program before and after hospitalized patients applied for the program. The systematic management of patients with food allergies according to a physician’s prescription provides the basis for safe meal preparation [5]. Agapito et al. propose DIETOS, a recommender system for adaptive delivery of nutritional content to improve the quality of life of healthy subjects and patients with diet-related chronic diseases. It is experimentally demonstrated that DIETOS is a novel food recommendation system for healthy people and people affected by diet-related chronic diseases. The system builds a user’s health profile and, therefore, provides personalized nutritional advice and pays attention to the geographic origin of foods [6]. These studies have certain reference value for promoting people’s daily healthy diet, but there is no clear explanation on how to formulate healthy diet recipes, and there is no relevant experimental verification. Therefore, in order to make up for the insufficiency of these studies, this paper reconstructs a recipe recommendation system for healthy eating.This paper has the following innovations in the research on healthy diet and its recommendation system. (1) The cold-start algorithm MCS4FPR is introduced into the diet system constructed in this paper, which not only enables the system to recommend recipes according to people’s health status, but also recommends healthy recipes with balanced nutrition and taste according to personal dietary preferences. (2) This paper also introduces the Internet of Things technology to make the recommendation system more intelligent to recommend healthy diets. It formulates reasonable and healthy eating arrangements for individuals and changes people’s unreasonable eating habits. (3) This paper analyzes the relative importance of each dimension of recipe information contained in the recommendation system, as well as the influence of age and gender on recipe recommendation. (4) The food culture recommendation system constructed in this paper is more efficient than the previous food recommendation system, and it is also more accurate than the previous one.


## 2. Method of Constructing a Recommendation System for Healthy Food Culture

### 2.1. Key Technologies of IoT Recommendation System

The Internet of Things technology is of great help to build a recommendation system for things, because the Internet of Things is the connection between things and the Internet, and the Internet is a global communication network and a huge international network [7]. The Internet of Things is connected with things on the basis of the Internet, connecting people and things, and can search for any desired information. The Internet of Things is shown in Figure 1.Figure 1
IoT.The basis of the recommender system is that it needs to have a large amount of information for screening, and select and recommend suitable information from it, so the Internet of Things is the support of the recommender system [8]. The Internet of Things can not only provide a large amount of information resources for the recommendation system, but also have the ability to intelligently process information and implement intelligent control of objects. The Internet of Things combines sensors and intelligent processing, and uses various intelligent technologies such as cloud computing and pattern recognition to provide technical support for recommendation systems [9]. In the flood of information in the Internet of Things, it is necessary to set the required information characteristics and so on. The first is to use the Internet of Things to filter information and constantly narrow the scope of information filtering until the most suitable information or solution is found. The recommendation process of the recommendation system is shown in Figure 2.Figure 2
Recommendation system and recommendation process.Recommendation systems can help users filter out a large amount of irrelevant information. Traditional methods of collaborative filtering recommendation and content-based recommendation are relatively intuitive and transparent. However, it is inevitable that most of the values are missing or zero data which makes the recommendation effect gradually decrease while the number of users is increasing [10]. Recommendation systems based on the Internet of Things generally include content-based recommendation algorithms, collaborative filtering algorithms, and combined recommendation algorithms. The advantages and disadvantages of these three algorithms are shown in Table 1.Table 1
Advantages and disadvantages of recommendation algorithms.
Recommendation algorithmAdvantageShortcomingContent-based recommendation algorithmNo need for users to access historical data, no storage in new objects and sparse problems, with mature classification learning supportLimited by feature extraction methods, there are new user questions and poor scalability, and training requires a large amount of data and high repetition rate of recommendation resultsCollaborative filtering recommendation algorithmWithout user rating data, it is easy to find users’ new interest points, and the increase of the number of users can improve the recommendation effectThere are sparse and cold-start issues, and poor scalability, and the quality of recommendations is limited by historical data setsCombined recommendation algorithmThe combination of the two methods effectively improves the recommendation effectThere are some challenges and difficulties in processing multi-source heterogeneous auxiliary information such as text and imageAmong them, the advantages of content-based recommendation algorithms are that users do not need to access historical data, do not need to store new objects and sparse problems, and have mature classification learning support. The combination algorithm refers to the combination of the content recommendation algorithm and the collaborative filtering algorithm, and the combination algorithm combines the advantages of the two algorithms. The content-based recommendation algorithm is that the user’s preferences will not change greatly within a certain period of time, and the core of the algorithm is information acquisition and information filtering [11]. In the traditional search engine in the Internet of Things, information retrieval is the main method to obtain information, and the text feature of information retrieval is generally word frequency-document frequency. Set the word frequency-document frequency as DF, assuming that when the user retrieves information, f is the frequency of the keyword k appearing in the DF, the definition formula of the word frequency SF is as follows:(1)SF=fgmax∗DF,where g represents the amount of information about the keyword k contained in the Internet of Things, and then the calculation of gmax is as follows:(2)gmax=SF∗∫fmax∗k.However, this method of searching information by keywords is still cumbersome, and it is impossible to distinguish whether the searched information has a strong correlation with the information required by the user [12]. Therefore, the inverted document frequency is introduced for searching, and the inverted document frequency is defined as(3)TDF=logfg.Then, the weight between document frequency and inverse document frequency can be expressed as(4)Wi=DF∗TDF.That being the case, the obtained document content can be expressed in terms of weights as(5)Contentxi=w1,w2,…,wi.Combined with the preferences of the information that users usually search for, the recommendation function based on the content recommendation algorithm can be expressed as(6)gk=scoreContent Based Profilek,Contentf∗Wi.The recommendation algorithm based on collaborative filtering can recommend specific items through the user’s historical information. It performs similarity analysis based on the preferences of the current user and other users, finds other users similar to the current user, and then predicts the preferences of the current user according to the user’s ratings of other items with high similarity. Finally, it recommends items with high item ratings to current users. This algorithm first needs to calculate the similarity between users, and the similarity is often calculated by the angle cosine similarity company. If the current user is a and the other users areb, the calculation formula of the similarity S is as follows:(7)Sa,b=cosa⟶,b⟶=a⟶⋅b⟶a2⋅b2.When the cosine similarity formula cannot be calculated, the Pearson correlation coefficient is generally used to calculate the similarity between users. The Pearson correlation coefficient formula is as follows:(8)S=a,b=∑i∈Nra−rb∑i∈Nrai−rb2∗∑i∈Nrbi−ra2,where r is the number of levels of ratings used by users in IoT and i is the threshold generated in IoT. In this way, recommending relevant information according to the similarity between users can improve the efficiency of the recommendation system, and this algorithm can also be adapted to the simultaneous use of large-scale users. The Internet of Things recommendation system needs to combine the content recommendation algorithm and the collaborative filtering algorithm; that is, the recommendation algorithm of the Internet of Things recommendation system adopts the combination algorithm. It can not only better recommend suitable information for users, but also adapt to large-scale users.


### 2.2. The Internet of Things and the Formulation of Healthy Recipes

With the development of today’s society, all kinds of snacks and beverages fill people’s daily food life. Although the food on the table is becoming more and more abundant, while people enjoy delicious food, the behavior of deviating from a balanced diet is becoming more and more prominent, and the number of health problems is also increasing [13]. Each region has a regionally unified dietary structure. For example, the dietary structure in the East is dominated by plant food and supplemented by animal food, but this dietary structure is prone to protein and energy malnutrition. The dietary structure of European and American countries is mainly based on animal food, and the dietary structure of balanced animal and plant food is represented by Japan. Therefore, the dietary structure of each region will have advantages and disadvantages. Moreover, a unified dietary structure will not fully meet all individuals in the region, so it is necessary to formulate healthy dietary recipes for individuals in a targeted manner [14]. To formulate healthy dietary recipes for individuals, it is necessary to understand the characteristics and nutritional value of each food, and this information can be obtained through the Internet of Things. The Internet of Things covers a large amount of information, among which the information about diet is even less common. In the Internet of Things, the attributes and various characteristics of food can be queried, as shown in Figure 3.Figure 3
Example of IoT search for food information.The formulation of healthy recipes requires a variety of information, on the one hand, the user’s own situation, and at the same time, it is necessary to refer to the dietary nutrition reference intake of residents. It determines the normal daily energy requirements of the human body and the supply of the three major thermogenic nutrients, and then needs to know the types and requirements of daily staple foods [15]. All of these are required to consult a large number of literature materials. If it just looks up paper materials, it will not only take a lot of time, but also be very cumbersome. And if it can find all kinds of food information it needs through the Internet of Things, it will provide convenience for the formulation of healthy recipes.Dietary structure, also known as dietary pattern, is a general expression of the quantity and proportion of various foods in the diet. It plays an important role in human growth and development. The formulation of healthy recipes by the Internet of Things needs to be formulated according to the dietary structure of the country and the physical condition of individuals, referring to the dietary knowledge map existing in the Internet of Things [16]. The diet knowledge graph in the Internet of Things has seven categories: recipes, efficacy, disease care, population, nutrients, ingredients, and TCM constitution. The dietary knowledge graph mode layer is shown in Figure 4.Figure 4
Diet knowledge graph schema layer.Using the Internet of Things technology to input personal physical conditions into the Internet of Things recipe customization system, the system will generate healthy recipes suitable for users according to the set principles. Assuming that the user’s age isM, the weight is X, and the height is R, the principle of generating the user’s personal physique through the Internet of Things system is as follows:(9)Q1=∫DTM∗X∗RD∗φ.Among them,φ is a matrix formed by various data of the human body, and its form is as follows:(10)φ=TDOG.In the formula,T is the arm length, D is the waist circumference, O is the leg length, and G is the balance data of each body item. The calculation method is as follows:(11)G=T+D+M+X+O+G6∗2.Then, the internal characteristics of the human body, such as blood pressure, are measured and predicted, the internal physique is obtained asQ2, and then the comprehensive physique of the user is calculated according to the following:(12)Q=∑Q1∗Q2∗∏φ.The user’s physical information received on the Internet of Things will automatically generate healthy diet recipes in combination with the diet knowledge graph. Although this generated diet recipe matches the user’s physique, there are many versions of the generated recipe. Users need to filter one by one until they find a recipe that matches their washing, which undoubtedly requires a lot of time and managers to do this step. So in order to solve this problem, it is necessary to combine IoT and personalized recommendation system to form a new healthy diet recommendation system.


### 2.3. Healthy Food Culture Recommendation System

Because IoT cannot take personal taste preferences into account, diet recipes formulated with the Internet of Things cannot achieve a balance between nutrition and hobbies, and there are various versions of recipes formulated through the Internet of Things, and it is difficult to select the version suitable for users. Therefore, a new healthy diet recommendation system is developed. This system can combine the Internet of Things recommendation system with the dietary knowledge graph, which can not only meet the daily nutritional needs of users, but also meet the hobbies of users [17, 18]. The function of this diet recommendation system is shown in Figure 5.Figure 5
Structure and function diagram of diet recommendation system.The background management system is mainly the management of user’s basic information. It realizes the operation of adding, deleting, modifying, and checking the basic information of users. The basic information includes name, age, gender, height, weight, and other physical indicators to generate a nutritious diet plan. The formulation of a nutritious diet plan needs to be based on personal basic information and formulas related to sports nutrition. First of all, it is necessary to calculate the static energy consumption value of the human body and set it asu, and then the calculation formula is as follows:(13)ug=R×6.25+X×10−M×5−161,ub=R×6.25+X×10−M×5+5.Among them,ug represents the static energy expenditure of women, ub represents the static energy expenditure of men, R represents the height, X represents the weight, and M represents the age. In addition to the basic information of the user’s body, the background management system also has the user’s preferences.The recipe recommendation client is divided into three modules, namely, recipe guide, recipe book, and community communication. The recipe guide in it has three functions. The first is to analyze the user’s diet according to the nutritional diet plan on the background management side to help users have a clear positioning of their diet. It then classifies the ingredients according to the three elements of nutrition required by the human body. Users can click to browse the ingredients of different categories and choose according to their own hobbies. Finally, it recommends various suitable recipes according to the user’s physique and preferences. The system generates a variety of recipes that conform to the user’s physique and preferences, and a combined recommendation algorithm is needed to screen out the most suitable personalized recipes from these recipes [19]. It sets the user to set the taste preference U and then counts the number E of these recipe documents. The principle recommended by the system is as follows:(14)IN=E•″U″∗uTDF.Among them, IN is the number of recipes left after the first round of recommendation, TDF is the inverted document frequency, and the system further recommends a personalized recipek. The principle is as follows:(15)k=IN⋅″U″⋅″C″∗uTDF.Among them, C is the user’s preferred cuisine, so the entire personalized recipe is in one place. In this way, both the nutrition required by the user’s body and the taste preference of the user can be satisfied. Then, the whole IoT healthy food culture recommendation system architecture is shown in Figure6.Figure 6
The overall architecture of the diet recommendation system.The healthy diet recommendation system under the Internet of Things technology can meet the individual needs of a large number of users, and the recommended recipes in this recommendation system have clear instructions for the nutritional content and trace elements of each food [20]. At the same time, the system also has a complete recipe module, which categorizes recipes according to tags into dishes, major cuisines, etc. It allows users to understand the information of ingredients in the system and improves users’ understanding of dietary health. And the recipes recommended by this healthy diet recommendation system can achieve a balance between nutrition and taste, and improve user satisfaction.


## 2.1. Key Technologies of IoT Recommendation System

The Internet of Things technology is of great help to build a recommendation system for things, because the Internet of Things is the connection between things and the Internet, and the Internet is a global communication network and a huge international network [7]. The Internet of Things is connected with things on the basis of the Internet, connecting people and things, and can search for any desired information. The Internet of Things is shown in Figure 1.Figure 1
IoT.The basis of the recommender system is that it needs to have a large amount of information for screening, and select and recommend suitable information from it, so the Internet of Things is the support of the recommender system [8]. The Internet of Things can not only provide a large amount of information resources for the recommendation system, but also have the ability to intelligently process information and implement intelligent control of objects. The Internet of Things combines sensors and intelligent processing, and uses various intelligent technologies such as cloud computing and pattern recognition to provide technical support for recommendation systems [9]. In the flood of information in the Internet of Things, it is necessary to set the required information characteristics and so on. The first is to use the Internet of Things to filter information and constantly narrow the scope of information filtering until the most suitable information or solution is found. The recommendation process of the recommendation system is shown in Figure 2.Figure 2
Recommendation system and recommendation process.Recommendation systems can help users filter out a large amount of irrelevant information. Traditional methods of collaborative filtering recommendation and content-based recommendation are relatively intuitive and transparent. However, it is inevitable that most of the values are missing or zero data which makes the recommendation effect gradually decrease while the number of users is increasing [10]. Recommendation systems based on the Internet of Things generally include content-based recommendation algorithms, collaborative filtering algorithms, and combined recommendation algorithms. The advantages and disadvantages of these three algorithms are shown in Table 1.Table 1
Advantages and disadvantages of recommendation algorithms.
Recommendation algorithmAdvantageShortcomingContent-based recommendation algorithmNo need for users to access historical data, no storage in new objects and sparse problems, with mature classification learning supportLimited by feature extraction methods, there are new user questions and poor scalability, and training requires a large amount of data and high repetition rate of recommendation resultsCollaborative filtering recommendation algorithmWithout user rating data, it is easy to find users’ new interest points, and the increase of the number of users can improve the recommendation effectThere are sparse and cold-start issues, and poor scalability, and the quality of recommendations is limited by historical data setsCombined recommendation algorithmThe combination of the two methods effectively improves the recommendation effectThere are some challenges and difficulties in processing multi-source heterogeneous auxiliary information such as text and imageAmong them, the advantages of content-based recommendation algorithms are that users do not need to access historical data, do not need to store new objects and sparse problems, and have mature classification learning support. The combination algorithm refers to the combination of the content recommendation algorithm and the collaborative filtering algorithm, and the combination algorithm combines the advantages of the two algorithms. The content-based recommendation algorithm is that the user’s preferences will not change greatly within a certain period of time, and the core of the algorithm is information acquisition and information filtering [11]. In the traditional search engine in the Internet of Things, information retrieval is the main method to obtain information, and the text feature of information retrieval is generally word frequency-document frequency. Set the word frequency-document frequency as DF, assuming that when the user retrieves information, f is the frequency of the keyword k appearing in the DF, the definition formula of the word frequency SF is as follows:(1)SF=fgmax∗DF,where g represents the amount of information about the keyword k contained in the Internet of Things, and then the calculation of gmax is as follows:(2)gmax=SF∗∫fmax∗k.However, this method of searching information by keywords is still cumbersome, and it is impossible to distinguish whether the searched information has a strong correlation with the information required by the user [12]. Therefore, the inverted document frequency is introduced for searching, and the inverted document frequency is defined as(3)TDF=logfg.Then, the weight between document frequency and inverse document frequency can be expressed as(4)Wi=DF∗TDF.That being the case, the obtained document content can be expressed in terms of weights as(5)Contentxi=w1,w2,…,wi.Combined with the preferences of the information that users usually search for, the recommendation function based on the content recommendation algorithm can be expressed as(6)gk=scoreContent Based Profilek,Contentf∗Wi.The recommendation algorithm based on collaborative filtering can recommend specific items through the user’s historical information. It performs similarity analysis based on the preferences of the current user and other users, finds other users similar to the current user, and then predicts the preferences of the current user according to the user’s ratings of other items with high similarity. Finally, it recommends items with high item ratings to current users. This algorithm first needs to calculate the similarity between users, and the similarity is often calculated by the angle cosine similarity company. If the current user is a and the other users areb, the calculation formula of the similarity S is as follows:(7)Sa,b=cosa⟶,b⟶=a⟶⋅b⟶a2⋅b2.When the cosine similarity formula cannot be calculated, the Pearson correlation coefficient is generally used to calculate the similarity between users. The Pearson correlation coefficient formula is as follows:(8)S=a,b=∑i∈Nra−rb∑i∈Nrai−rb2∗∑i∈Nrbi−ra2,where r is the number of levels of ratings used by users in IoT and i is the threshold generated in IoT. In this way, recommending relevant information according to the similarity between users can improve the efficiency of the recommendation system, and this algorithm can also be adapted to the simultaneous use of large-scale users. The Internet of Things recommendation system needs to combine the content recommendation algorithm and the collaborative filtering algorithm; that is, the recommendation algorithm of the Internet of Things recommendation system adopts the combination algorithm. It can not only better recommend suitable information for users, but also adapt to large-scale users.


## 2.2. The Internet of Things and the Formulation of Healthy Recipes

With the development of today’s society, all kinds of snacks and beverages fill people’s daily food life. Although the food on the table is becoming more and more abundant, while people enjoy delicious food, the behavior of deviating from a balanced diet is becoming more and more prominent, and the number of health problems is also increasing [13]. Each region has a regionally unified dietary structure. For example, the dietary structure in the East is dominated by plant food and supplemented by animal food, but this dietary structure is prone to protein and energy malnutrition. The dietary structure of European and American countries is mainly based on animal food, and the dietary structure of balanced animal and plant food is represented by Japan. Therefore, the dietary structure of each region will have advantages and disadvantages. Moreover, a unified dietary structure will not fully meet all individuals in the region, so it is necessary to formulate healthy dietary recipes for individuals in a targeted manner [14]. To formulate healthy dietary recipes for individuals, it is necessary to understand the characteristics and nutritional value of each food, and this information can be obtained through the Internet of Things. The Internet of Things covers a large amount of information, among which the information about diet is even less common. In the Internet of Things, the attributes and various characteristics of food can be queried, as shown in Figure 3.Figure 3
Example of IoT search for food information.The formulation of healthy recipes requires a variety of information, on the one hand, the user’s own situation, and at the same time, it is necessary to refer to the dietary nutrition reference intake of residents. It determines the normal daily energy requirements of the human body and the supply of the three major thermogenic nutrients, and then needs to know the types and requirements of daily staple foods [15]. All of these are required to consult a large number of literature materials. If it just looks up paper materials, it will not only take a lot of time, but also be very cumbersome. And if it can find all kinds of food information it needs through the Internet of Things, it will provide convenience for the formulation of healthy recipes.Dietary structure, also known as dietary pattern, is a general expression of the quantity and proportion of various foods in the diet. It plays an important role in human growth and development. The formulation of healthy recipes by the Internet of Things needs to be formulated according to the dietary structure of the country and the physical condition of individuals, referring to the dietary knowledge map existing in the Internet of Things [16]. The diet knowledge graph in the Internet of Things has seven categories: recipes, efficacy, disease care, population, nutrients, ingredients, and TCM constitution. The dietary knowledge graph mode layer is shown in Figure 4.Figure 4
Diet knowledge graph schema layer.Using the Internet of Things technology to input personal physical conditions into the Internet of Things recipe customization system, the system will generate healthy recipes suitable for users according to the set principles. Assuming that the user’s age isM, the weight is X, and the height is R, the principle of generating the user’s personal physique through the Internet of Things system is as follows:(9)Q1=∫DTM∗X∗RD∗φ.Among them,φ is a matrix formed by various data of the human body, and its form is as follows:(10)φ=TDOG.In the formula,T is the arm length, D is the waist circumference, O is the leg length, and G is the balance data of each body item. The calculation method is as follows:(11)G=T+D+M+X+O+G6∗2.Then, the internal characteristics of the human body, such as blood pressure, are measured and predicted, the internal physique is obtained asQ2, and then the comprehensive physique of the user is calculated according to the following:(12)Q=∑Q1∗Q2∗∏φ.The user’s physical information received on the Internet of Things will automatically generate healthy diet recipes in combination with the diet knowledge graph. Although this generated diet recipe matches the user’s physique, there are many versions of the generated recipe. Users need to filter one by one until they find a recipe that matches their washing, which undoubtedly requires a lot of time and managers to do this step. So in order to solve this problem, it is necessary to combine IoT and personalized recommendation system to form a new healthy diet recommendation system.


## 2.3. Healthy Food Culture Recommendation System

Because IoT cannot take personal taste preferences into account, diet recipes formulated with the Internet of Things cannot achieve a balance between nutrition and hobbies, and there are various versions of recipes formulated through the Internet of Things, and it is difficult to select the version suitable for users. Therefore, a new healthy diet recommendation system is developed. This system can combine the Internet of Things recommendation system with the dietary knowledge graph, which can not only meet the daily nutritional needs of users, but also meet the hobbies of users [17, 18]. The function of this diet recommendation system is shown in Figure 5.Figure 5
Structure and function diagram of diet recommendation system.The background management system is mainly the management of user’s basic information. It realizes the operation of adding, deleting, modifying, and checking the basic information of users. The basic information includes name, age, gender, height, weight, and other physical indicators to generate a nutritious diet plan. The formulation of a nutritious diet plan needs to be based on personal basic information and formulas related to sports nutrition. First of all, it is necessary to calculate the static energy consumption value of the human body and set it asu, and then the calculation formula is as follows:(13)ug=R×6.25+X×10−M×5−161,ub=R×6.25+X×10−M×5+5.Among them,ug represents the static energy expenditure of women, ub represents the static energy expenditure of men, R represents the height, X represents the weight, and M represents the age. In addition to the basic information of the user’s body, the background management system also has the user’s preferences.The recipe recommendation client is divided into three modules, namely, recipe guide, recipe book, and community communication. The recipe guide in it has three functions. The first is to analyze the user’s diet according to the nutritional diet plan on the background management side to help users have a clear positioning of their diet. It then classifies the ingredients according to the three elements of nutrition required by the human body. Users can click to browse the ingredients of different categories and choose according to their own hobbies. Finally, it recommends various suitable recipes according to the user’s physique and preferences. The system generates a variety of recipes that conform to the user’s physique and preferences, and a combined recommendation algorithm is needed to screen out the most suitable personalized recipes from these recipes [19]. It sets the user to set the taste preference U and then counts the number E of these recipe documents. The principle recommended by the system is as follows:(14)IN=E•″U″∗uTDF.Among them, IN is the number of recipes left after the first round of recommendation, TDF is the inverted document frequency, and the system further recommends a personalized recipek. The principle is as follows:(15)k=IN⋅″U″⋅″C″∗uTDF.Among them, C is the user’s preferred cuisine, so the entire personalized recipe is in one place. In this way, both the nutrition required by the user’s body and the taste preference of the user can be satisfied. Then, the whole IoT healthy food culture recommendation system architecture is shown in Figure6.Figure 6
The overall architecture of the diet recommendation system.The healthy diet recommendation system under the Internet of Things technology can meet the individual needs of a large number of users, and the recommended recipes in this recommendation system have clear instructions for the nutritional content and trace elements of each food [20]. At the same time, the system also has a complete recipe module, which categorizes recipes according to tags into dishes, major cuisines, etc. It allows users to understand the information of ingredients in the system and improves users’ understanding of dietary health. And the recipes recommended by this healthy diet recommendation system can achieve a balance between nutrition and taste, and improve user satisfaction.


## 3. Experiment and Analysis of Healthy Food Culture Recommendation System

### 3.1. Recommendation Accuracy Rate of the Recommendation System

Among the nutrients needed in the human body, protein, fat, and carbohydrates are nutrients that can generate heat in the body during the process of human metabolism. It is an indispensable nutrient in the human body [21, 22]. At the same time, these nutrients must be ingested in a certain proportion. If too much or too little is ingested, it will affect the absorption of trace elements. In this experiment, 10 students aged 11–13 (5 males and 5 males) will be selected from a school for the experiment. Table 2 shows the standard daily intake of some nutrients for 11–13 years old.Table 2
Standard daily intake of some nutrients for 11–13 years old.
Energy or nutrientsRNIAMDRMaleFemaleHeat (kcal/d)26002100–2300—Carbohydrate——50–65Fat——20–30Protein (k/kg)——0.75Vc (mg/d)80–100Using the recommendation system in this paper, the actual daily intake of these ten students was analyzed and compared with the daily standard intake, as shown in Figure7.Figure 7
Comparison of nutrient intake. (a) Boy. (b) Girl.
(a)(b)From (Figure7(b)), it can be seen that the caloric intake of male students is basically unbalanced with the normal caloric intake. In particular, the daily calorie intake of the second and third male students far exceeds the normal intake. Among them, the carbohydrate intake of the second and third students far exceeded the normal intake. Protein intake is also seriously out of the reasonable range. The calorie intake of the tenth student in (Figure 7(b)) far exceeds the standard intake. The fat intake of all female classmates is seriously exceeding the standard, so the diet of these ten classmates is unreasonable, which will affect their health more or less. To this end, this experiment also checked some of the physical indicators of these ten students. The data of physical indicators are shown in Table 3.Table 3
Data on physical indicators of ten students.
StudentHeight (cm)Weight (kg)Cardiac rateBlood pressureSBPDBP11546665100702145567085563156575778894168566913786518945891458361566767135677145675812867815045671678591603667126571016756588480The weight of junior high school students is generally between 45–50, but from Table3, most of the students are somewhat obese. And compared with the standard blood pressure value, there are some mild hypertension symptoms, so unreasonable diet will endanger health. To this end, we will make healthy eating recipes for these ten students according to their physical conditions and their respective taste preferences. In the recommendation system, the physical health and hobbies of these ten students are entered into the system, and then the system will generate personalized recipes for these ten students. We recorded the efficiency of the recommendation system and the students’ liking of the food in the recommended recipes, as shown in Figure 8.Figure 8
Performance analysis of recommender system.Fitness refers to the degree to which the recipe recommended by the recommender system matches the student’s physique [23]. In Figure 8, it can be seen that the recommendation system takes about an hour to recommend and create personalized recipes, which is much faster than the previous manual formulation of healthy recipes. And these ten students have high satisfaction with the diet recipes generated by the recommendation system, which are also very suitable for the students’ physique. Therefore, the recommendation system has high accuracy for the recommendation of personalized recipes [24, 25].


### 3.2. Health Effects of Recommended Recipes

In order to verify that the personalized recipes recommended by the healthy diet recommendation system in this paper can promote human health, ten students from the school were specially monitored for one year. During the year, the ten students carried out daily eating activities according to the personalized recipes recommended by the recommendation system and recorded various physical indicators of the ten students one year later. It is compared with the previously recorded indicator data to verify the effect of the recommended recipes on human health. The normal data of various physical indicators for 11–13 years old are shown in Table4.Table 4
Standard data of various physical indicators for 11–13 years old.
AgeHeight (cm)Weight (kg)Cardiac rateBlood pressureBoyGirlBoyGirlSBP (mmHg)DBP (mmHg)11145146373660–10090–14060–901215014542.4940.77131591564844The comparison of the physical indicators of these ten students after one year and the physical indicator data of one year ago is shown in Figure9.Figure 9
Comparison of body index data. (a) Heart rate index for boys. (b) Heart rate index for girls. (c) Blood pressure index for boys. (d) Blood pressure index for girls.
(a)(b)(c)(d)From (Figures9(a) and 9(b)), it can be seen that the heart rate indicators of these ten students are basically within the standard range. From (Figures 9(c) and 9(d)), the blood pressure of these ten students is already within the normal blood pressure range. Therefore, the recipes recommended by the healthy diet recommendation system constructed in this paper are of great help to people’s healthy diet, and the recipes introduced are also more reasonable.


### 3.3. Experimental Summary

Experiments show that the healthy diet recommendation system constructed in this paper can analyze the user’s physique in detail, including various data of physical indicators. It can make personalized recommendations based on the user’s hobbies, users have high satisfaction with the recommended recipes, and the satisfaction rate can reach more than 90%. And through the physical changes of ten classmates, it can be found that the recipes recommended by the recommendation system in this article can effectively improve the students’ physical fitness, so the recipes recommended by the recommendation system can make the user’s diet more rational [26].


## 3.1. Recommendation Accuracy Rate of the Recommendation System

Among the nutrients needed in the human body, protein, fat, and carbohydrates are nutrients that can generate heat in the body during the process of human metabolism. It is an indispensable nutrient in the human body [21, 22]. At the same time, these nutrients must be ingested in a certain proportion. If too much or too little is ingested, it will affect the absorption of trace elements. In this experiment, 10 students aged 11–13 (5 males and 5 males) will be selected from a school for the experiment. Table 2 shows the standard daily intake of some nutrients for 11–13 years old.Table 2
Standard daily intake of some nutrients for 11–13 years old.
Energy or nutrientsRNIAMDRMaleFemaleHeat (kcal/d)26002100–2300—Carbohydrate——50–65Fat——20–30Protein (k/kg)——0.75Vc (mg/d)80–100Using the recommendation system in this paper, the actual daily intake of these ten students was analyzed and compared with the daily standard intake, as shown in Figure7.Figure 7
Comparison of nutrient intake. (a) Boy. (b) Girl.
(a)(b)From (Figure7(b)), it can be seen that the caloric intake of male students is basically unbalanced with the normal caloric intake. In particular, the daily calorie intake of the second and third male students far exceeds the normal intake. Among them, the carbohydrate intake of the second and third students far exceeded the normal intake. Protein intake is also seriously out of the reasonable range. The calorie intake of the tenth student in (Figure 7(b)) far exceeds the standard intake. The fat intake of all female classmates is seriously exceeding the standard, so the diet of these ten classmates is unreasonable, which will affect their health more or less. To this end, this experiment also checked some of the physical indicators of these ten students. The data of physical indicators are shown in Table 3.Table 3
Data on physical indicators of ten students.
StudentHeight (cm)Weight (kg)Cardiac rateBlood pressureSBPDBP11546665100702145567085563156575778894168566913786518945891458361566767135677145675812867815045671678591603667126571016756588480The weight of junior high school students is generally between 45–50, but from Table3, most of the students are somewhat obese. And compared with the standard blood pressure value, there are some mild hypertension symptoms, so unreasonable diet will endanger health. To this end, we will make healthy eating recipes for these ten students according to their physical conditions and their respective taste preferences. In the recommendation system, the physical health and hobbies of these ten students are entered into the system, and then the system will generate personalized recipes for these ten students. We recorded the efficiency of the recommendation system and the students’ liking of the food in the recommended recipes, as shown in Figure 8.Figure 8
Performance analysis of recommender system.Fitness refers to the degree to which the recipe recommended by the recommender system matches the student’s physique [23]. In Figure 8, it can be seen that the recommendation system takes about an hour to recommend and create personalized recipes, which is much faster than the previous manual formulation of healthy recipes. And these ten students have high satisfaction with the diet recipes generated by the recommendation system, which are also very suitable for the students’ physique. Therefore, the recommendation system has high accuracy for the recommendation of personalized recipes [24, 25].


## 3.2. Health Effects of Recommended Recipes

In order to verify that the personalized recipes recommended by the healthy diet recommendation system in this paper can promote human health, ten students from the school were specially monitored for one year. During the year, the ten students carried out daily eating activities according to the personalized recipes recommended by the recommendation system and recorded various physical indicators of the ten students one year later. It is compared with the previously recorded indicator data to verify the effect of the recommended recipes on human health. The normal data of various physical indicators for 11–13 years old are shown in Table4.Table 4
Standard data of various physical indicators for 11–13 years old.
AgeHeight (cm)Weight (kg)Cardiac rateBlood pressureBoyGirlBoyGirlSBP (mmHg)DBP (mmHg)11145146373660–10090–14060–901215014542.4940.77131591564844The comparison of the physical indicators of these ten students after one year and the physical indicator data of one year ago is shown in Figure9.Figure 9
Comparison of body index data. (a) Heart rate index for boys. (b) Heart rate index for girls. (c) Blood pressure index for boys. (d) Blood pressure index for girls.
(a)(b)(c)(d)From (Figures9(a) and 9(b)), it can be seen that the heart rate indicators of these ten students are basically within the standard range. From (Figures 9(c) and 9(d)), the blood pressure of these ten students is already within the normal blood pressure range. Therefore, the recipes recommended by the healthy diet recommendation system constructed in this paper are of great help to people’s healthy diet, and the recipes introduced are also more reasonable.


## 3.3. Experimental Summary

Experiments show that the healthy diet recommendation system constructed in this paper can analyze the user’s physique in detail, including various data of physical indicators. It can make personalized recommendations based on the user’s hobbies, users have high satisfaction with the recommended recipes, and the satisfaction rate can reach more than 90%. And through the physical changes of ten classmates, it can be found that the recipes recommended by the recommendation system in this article can effectively improve the students’ physical fitness, so the recipes recommended by the recommendation system can make the user’s diet more rational [26].


## 4. Discussion

This article introduces that the Internet of Things is composed of the Internet and the connection of things. It can get a lot of information by using the Internet of Things, so it is very important to use the Internet of Things technology in the recommendation system. The recommendation system can recommend the information needed by the user according to the keywords of the desired information, which can greatly improve the information over-illumination rate of the user and help the user save a lot of information search time. In the daily network search, the historical information of the search will be left, and the recommendation system can analyze the user’s recent preferences according to the historical search information and recommend the content according to the user’s preferences. The Internet of Things recommendation system in this paper can search for information through keywords and can set the items that need to be made through programming. The application of the Internet of Things recommendation system can be used in various fields, and to a certain extent, it can promote the process of the project and improve the efficiency of work.The healthy food culture recommendation system designed in this paper is based on the Internet of Things recommendation system. In this paper, based on the recommendation of the Internet of Things system, the combined recommendation algorithm is applied to the system to be constructed, and the knowledge map of healthy eating is introduced into the system. In this way, the recommendation system will have a lot of information about various kinds of ingredients. This information includes nutrients, growing conditions, and more. When the diet recommendation system is in operation, users can input their own physique and hobbies and other limited conditions into the constructed healthy diet recommendation system at the same time, and the system can help users recommend personalized recipes. A healthy diet can promote human health, so people’s daily diet needs to be reasonable to promote good health.And the experiment of this paper shows that the healthy food culture recommendation system constructed in this paper can promote the scientificity of people’s diet and improve people’s physique. It also allows recipes to strike a balance between nutrition and user preferences. This healthy diet recommendation system can also be further studied to formulate different healthy meals for people’s three meals a day, which can promote the abundance of food on people’s table. At the same time, the recommended food list is based on the user’s physical condition and hobbies, so it is of great help to the user’s daily reasonable diet.


## 5. Conclusion

This paper expounds the advantages and disadvantages of the recommendation system under the Internet of Things technology and improves it according to the advantages and disadvantages of the recommendation system. This paper then introduces the recommendation algorithm into the recommendation system under the Internet of Things technology, so that the recommendation system under the Internet of Things technology is more optimized. This paper then re-inputs all the information about recipes into the recommendation system of the Internet of Things and builds a new recommendation system for the healthy food culture of the Internet of Things. Experiments show that the healthy diet recommendation system in this paper can recommend suitable personalized recipes for users, which can not only satisfy the user’s taste preferences, but also ensure the nutrition and health of the recipes. The healthy food culture recommendation system constructed in this paper has great practical value for people’s daily healthy diet. However, the healthy diet recommendation system constructed in this paper cannot detect the physical changes of users in real time to adjust healthy recipes. It is hoped that future research can focus on changing this shortcoming.



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*Source: 9675452-2022-08-31.xml*