# A Method of Recommending Physical Education Network Course Resources Based on Machine Learning Algorithms

Authors: Fu Wei
Journal: Security and Communication Networks
 (2021)

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## Abstract

Aiming at the problem of difficult selection of physical education online course resources, a method of recommending online course resources based on machine learning algorithms is proposed. The information recommendation model is established through the expression of a collaborative filtering algorithm and resource feedback matrix. According to the feedback score of any user on the same data resource in the project set, the interest matching degree is established by comparative analysis, and the matching degree is substituted into the cosine similarity function to calculate the similarity threshold between each item and so on, calculate the similarity threshold number of all items, select the project resource that best matches the user according to the threshold number, and complete the recommendation. The experimental results show that the recommended method of physical education network curriculum resources based on machine learning algorithm is relatively excellent in recommendation accuracy and efficiency; this method can realize the innovation of higher physical education network curriculum teaching mode.


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## Body

## 1. Introduction

With the rapid development of Internet and mobile Internet technologies, online learning methods have been widely recognized by people. While online learning websites provide users with rich curriculum resources, the problem of information overload has become increasingly prominent, and massive learning resources often make users at a loss. The recommendation system can help users quickly find the courses they need, and it is one of the most effective ways to alleviate the problem of information overload [1]. At present, due to the lack of uniform standards for the construction and classification of online teaching resources, the design of online physical education resources is mostly developed based on personal experience. However, there are many types of sports in the sports discipline. Each link must collect relevant information about each sport in the development, which cannot be solved by a few network developers and one or two sports experts. Therefore, in the development process, a unified standard should be adopted to identify each knowledge point of each sports event and the format of teaching resources. At the same time, various development units, especially colleges and universities, have serious “standardism” to thought [2]. Many colleges and universities have built their own network teaching platform using their own network out of the consideration of increasing the popularity and reputation of the school brand or out of the consideration of the development and integration of the school’s teaching resources. This has led to repeated investment in hardware facilities, the repeated effect of software platforms, and repeated construction of online sports courses so that there are multiple “versions” of physical education resources for the same sports item and content. Because there is no unified logo, it is difficult to maintain later. The dilemma of expansion makes the utilization rate of online physical education resources very low [3]. If a unified platform could be utilized, this platform could be compatible with other data types and have a unified recommendation algorithm so that data across the network could be unified and more resources could be available for more efficient learning.At present, relevant scholars have researched the recommended physical education network course resources and have obtained corresponding research results. Literature [4] discusses the witty construction path of the regional college physical education union’s collaborative development and proposes a strategy for the regional college sports teaching alliances to achieve normalized and coordinated development in the new era to implement the spirit of “coconstruction, cogovernance, and sharing,” promote the comprehensive integration and efficient use of regional college physical education resources, and promote the coordinated development of regional college physical education in the new era. Literature [5] proposes the application of the decision tree algorithm in the analysis and management of physical education courses. First, analyze the data mining method used-C4.5 decision tree algorithm, and then give the course analysis and management system framework and its database design. The above method is feasible, but there is still room for improvement in the accuracy of resource recommendations.Aiming at the problems of the above methods, this article proposes a method of recommending physical education network course resources based on machine learning algorithms. The main contributions of this paper are as follows:(1)
Using machine learning methods to process the data helps to mine more features of the data, which is helpful for the subsequent recommendation(2)
Using a collaborative filtering model algorithm to calculate the similarity of datasets can effectively distinguish similar data and efficiently recommend physical education online course resources(3)
Experiments prove that our method is comparable or even better compared with other methodsThe list of symbols is shown in Table1.Table 1
List of symbols.
SymbolsDescriptionRm,nScoring data matrixsimu,ujSimilarity formulaKNNuNearest neighbor setG=V,EBipartite graph networkA=αulM,NAdjacency matrix represents the relationship between users and itemsαu,i=1User selects itemuαul=2User cannot select itemuS=l1,l3The set with the initial resource value of 1P=u1,u2,u3The user setLNode informationUAdjacent nodesαu,lInitial resource valuervThe process that has not selected project resourcesrxThe process of obtaining the project resourceRIi,jScore the same data for useri and user jNRIiTarget itemPi,kScore of item collectionkPk¯,Rn¯The average of the predicted scores of all user item setsk and nRi,kThe actual score of sports resource information databasekUIi,jThe intersection between user ratingsi and jNUIiA large-scale user setRMThe numerical root mean squareru,riThe training matrix of the training set and the test setCThe number of datasetsTMThe calculated value


## 2. Recommendation Algorithm Based on Machine Learning

### 2.1. Collaborative Filtering Algorithm

#### 2.1.1. Information Classification Processing Based on Collaborative Filtering Algorithm

Use preprocessing technology and data mining technology to process and classify the information resources in the node [6], and then build a scoring data model. Generally choose to use M×N scoring matrixR:(1)Rm,n=r1r2⋯rnr11r12⋯r1n⋮⋮⋱⋮rm1rm2⋯rmn.Among them,M represents the number of rows of the matrix, N represents the number of columns in the matrix, and the evaluation value of item j by data i is represented by Rij. An integer between 0 and 5 represents the data evaluation value. As the score of the data onto the item, the larger the value, the higher the popularity of the data, and 0 means that the data is not evaluated. You can also use an integer interval to indicate the evaluation level; the higher the level, the more popular it is.Use the constructed scoring data model to search the network information data and obtain the scoring vector of the scoring information on the network information data. The score vector is calculated according to the similarity formula, and the score similaritysimu,uj between data u and data uj in the data scoring matrix is obtained. Then, sort the obtained node information similarity in descending order, and we can get simu,u1>simu,u2,>⋯>simu,ux. Then, the size of the nearest neighbor value is determined according to the selection strategy. There are two main forms of selecting neighbor strategies. The first is to set the number of nearest neighbors to k in advance, and the second is to set a fixed similarity threshold. If the target similarity is greater than the threshold, the corresponding data can be divided into the target’s nearest neighbor set. However, long-term experience has proved that the method of determining the nearest neighbor value of a fixed value k is better than the latter. Therefore, a fixed number of neighbors of k are generally adopted to confirm the number of most similar neighbors. The nearest neighbor set can be expressed as KNNu=u1,u2,…uk. u∉KNNu,k∈1,m.We use k-means here [7]. Of course, there are now many evolved versions of the k-means algorithm, such as k-means ++ [8], and we will use the original k-means algorithm here because it has many advantages, for example, the algorithm guarantees better scalability when dealing with large datasets and the algorithm is of low complexity.


#### 2.1.2. Similarity Calculation

In the collaborative filtering algorithm, the calculation of the nearest similarity is an important prerequisite for finding the neighbor set [9]. The similarity calculation function is divided into cosine similarity, modified cosine similarity, and correlation similarity [10]. Among them, the cosine similarity is to calculate the cosine value of the inner product angle between the network node information and the item space vector and this cosine value is used to calculate the similarity between the data [11]; the process is as follows:(2)simu,ν=cosu¯,ν¯=u¯,ν¯u¯||ν¯=∑i∈Iuνruirνi∑i∈Iuνrui2∑i∈Iuνrvi2.The purpose of adjusting the cosine similarity is to overcome the problem of different scoring scales caused by the influence of individual factors in the evaluation of network information data. The modification on the cosine similarity formula is mainly to calculate the cosine of the included angle after subtracting the obtained cosine similarity vector from the average score vector of the information data. The process is as follows:(3)simu,v=∑i∈Iuνrui−ru¯rvi−rv¯∑i∈Iurui−ru¯2∑i∈Iνrvi−rv¯2.Correlation similarity is also called Pearson’s coefficient. First, find the set of items whose information data has been scored jointly, and then calculate the correlation coefficient of the vector in the set of common score items to express the similarity in different network information data, as shown in the following formula:(4)sim=∑i∈Iuirui−ru¯rvi−rv¯∑i∈Iuvrui−ru¯2∑i∈Iuvrvi−rv¯2.In the above three similarity calculation formulas,Iu, Iv are the scored item sets of network information data u, v, Iu, Iv, and Iu, Iv are the number of elements, and the intersection of the scored item sets is Iuv=IU∩Iv, ru¯=∑i∈Iurui/Iu, rv¯=∑i∈Ivrvi/Iv, and uv, which is the average rating of the evaluation item. In fact, these three similarity calculation methods are calculating the similarity in the scores of the data. If the volume of the information data is not large, there will be related problems that will affect the accuracy of the calculation. Therefore, different similarity calculations should be selected according to different datasets. Method to ensure the accuracy of the calculation.After the node information is classified based on the collaborative filtering algorithm, the bipartite graph network structure is used to allocate the resources of the classified node information and recommend the information resources [12]. Suppose a bipartite graph network is represented by G=V,E, U represents user categories of classified information, and L represents item categories of classified information. There are U types of data information nodes and L types of data information nodes and the data information edge set between them. E forms a network structure. If it is assumed that there are N users L=l1,l2,…lN and M items U=u1,u2,…uM in this network structure, then a selection relationship will be formed into users and items, and an adjacency matrix A=αulM,N can be used to represent this relationship. If the user selects an item u, then αu,i=1; if not, αul=2.Every user has their own hobbies, so according to the items selected by the user, we can accurately understand the user’s likes and dislikes for certain items at this stage [13]. At this time, the score clearly shows the user’s preference for each item. If these different preferences in the abstract sense are extracted (transmissible information, information resource value, etc.) and then represented concretely, it can be clearly seen that different items selected by different users can be connected to form a network of relationships. Let the obtained resource value flow in these relationships. When the target user chosen assigns the resource value obtained by the unselected user to the user’s unselected item, the target user has the unselected item of the recommended user, and the recommendation process is formed [14]. Multinode information resource recommendation process of bipartite graph network structure is shown in Figure 1.Figure 1
Multinode information resource recommendation processes of the bipartite graph network structure. (a) Assign initial resource value. (b) First step resource allocations. (c) The second step resource allocations.As shown in Figure1, assuming that the target user is u1, the multinode information resource allocation process of the bipartite graph is as follows:(1)
Set initial resource values for information data items. Set “1” as the initial resource value of the item selected by the target useru1 in the type L node information, and the item that is not selected is 2. The project set with the initial resource value of 1 is S=l1,l3.(2)
According to the selected relationship, the resource value assigned to theS set is integrated and distributed to the U adjacent nodes, so that the user set P=u1,u2,u3 can obtain the resource value. The resource value obtained by all users in user set P is the sum of the resource values of all project nodes l in set S divided by the median of node l.(3)
According to the selected relationship, first integrate the recommended resource values in theP sets, then assign the integrated user node resource values to the adjacent category l item nodes in the P sets, and finally get the information item. All items that are not selected will be included in the resource value. In this case, the obtained type l information node resource value includes the sum of all users’ information item resource values u in the user set P minus l information node degrees.For the resource diffusion process proposed by the multinode information resource allocation algorithm based on the bipartite graph network structure, after setting the target user asu, the specific steps are as follows:(1)
The initial resource value is determined, expressed by the following formula:(5)au,l=1,if useruchoose item l,u∈Uandl∈L,2,otherwise.Among them, the information iteml corresponding to the user u is the set initial resource value represented by αu,l.(2)
Select any userv from the P sets that has not selected project resources as the selected target user u; the process is as follows:(6)rv=∑l∈Lau,lavlαl,V∈U.Among them,Dl represents how many users have selected information item l, that is, the degree of this information item.(3)
The process of obtaining the resource of any itemx in the P set is expressed by the following formula:(7)rx=∑v∈Urvav,xdv.Among them,rx is the recommended resource value generated by item x when target user u recommends item x. The number of items dv selected by any user v is the degree of any user v.


## 2.1. Collaborative Filtering Algorithm

### 2.1.1. Information Classification Processing Based on Collaborative Filtering Algorithm

Use preprocessing technology and data mining technology to process and classify the information resources in the node [6], and then build a scoring data model. Generally choose to use M×N scoring matrixR:(1)Rm,n=r1r2⋯rnr11r12⋯r1n⋮⋮⋱⋮rm1rm2⋯rmn.Among them,M represents the number of rows of the matrix, N represents the number of columns in the matrix, and the evaluation value of item j by data i is represented by Rij. An integer between 0 and 5 represents the data evaluation value. As the score of the data onto the item, the larger the value, the higher the popularity of the data, and 0 means that the data is not evaluated. You can also use an integer interval to indicate the evaluation level; the higher the level, the more popular it is.Use the constructed scoring data model to search the network information data and obtain the scoring vector of the scoring information on the network information data. The score vector is calculated according to the similarity formula, and the score similaritysimu,uj between data u and data uj in the data scoring matrix is obtained. Then, sort the obtained node information similarity in descending order, and we can get simu,u1>simu,u2,>⋯>simu,ux. Then, the size of the nearest neighbor value is determined according to the selection strategy. There are two main forms of selecting neighbor strategies. The first is to set the number of nearest neighbors to k in advance, and the second is to set a fixed similarity threshold. If the target similarity is greater than the threshold, the corresponding data can be divided into the target’s nearest neighbor set. However, long-term experience has proved that the method of determining the nearest neighbor value of a fixed value k is better than the latter. Therefore, a fixed number of neighbors of k are generally adopted to confirm the number of most similar neighbors. The nearest neighbor set can be expressed as KNNu=u1,u2,…uk. u∉KNNu,k∈1,m.We use k-means here [7]. Of course, there are now many evolved versions of the k-means algorithm, such as k-means ++ [8], and we will use the original k-means algorithm here because it has many advantages, for example, the algorithm guarantees better scalability when dealing with large datasets and the algorithm is of low complexity.


### 2.1.2. Similarity Calculation

In the collaborative filtering algorithm, the calculation of the nearest similarity is an important prerequisite for finding the neighbor set [9]. The similarity calculation function is divided into cosine similarity, modified cosine similarity, and correlation similarity [10]. Among them, the cosine similarity is to calculate the cosine value of the inner product angle between the network node information and the item space vector and this cosine value is used to calculate the similarity between the data [11]; the process is as follows:(2)simu,ν=cosu¯,ν¯=u¯,ν¯u¯||ν¯=∑i∈Iuνruirνi∑i∈Iuνrui2∑i∈Iuνrvi2.The purpose of adjusting the cosine similarity is to overcome the problem of different scoring scales caused by the influence of individual factors in the evaluation of network information data. The modification on the cosine similarity formula is mainly to calculate the cosine of the included angle after subtracting the obtained cosine similarity vector from the average score vector of the information data. The process is as follows:(3)simu,v=∑i∈Iuνrui−ru¯rvi−rv¯∑i∈Iurui−ru¯2∑i∈Iνrvi−rv¯2.Correlation similarity is also called Pearson’s coefficient. First, find the set of items whose information data has been scored jointly, and then calculate the correlation coefficient of the vector in the set of common score items to express the similarity in different network information data, as shown in the following formula:(4)sim=∑i∈Iuirui−ru¯rvi−rv¯∑i∈Iuvrui−ru¯2∑i∈Iuvrvi−rv¯2.In the above three similarity calculation formulas,Iu, Iv are the scored item sets of network information data u, v, Iu, Iv, and Iu, Iv are the number of elements, and the intersection of the scored item sets is Iuv=IU∩Iv, ru¯=∑i∈Iurui/Iu, rv¯=∑i∈Ivrvi/Iv, and uv, which is the average rating of the evaluation item. In fact, these three similarity calculation methods are calculating the similarity in the scores of the data. If the volume of the information data is not large, there will be related problems that will affect the accuracy of the calculation. Therefore, different similarity calculations should be selected according to different datasets. Method to ensure the accuracy of the calculation.After the node information is classified based on the collaborative filtering algorithm, the bipartite graph network structure is used to allocate the resources of the classified node information and recommend the information resources [12]. Suppose a bipartite graph network is represented by G=V,E, U represents user categories of classified information, and L represents item categories of classified information. There are U types of data information nodes and L types of data information nodes and the data information edge set between them. E forms a network structure. If it is assumed that there are N users L=l1,l2,…lN and M items U=u1,u2,…uM in this network structure, then a selection relationship will be formed into users and items, and an adjacency matrix A=αulM,N can be used to represent this relationship. If the user selects an item u, then αu,i=1; if not, αul=2.Every user has their own hobbies, so according to the items selected by the user, we can accurately understand the user’s likes and dislikes for certain items at this stage [13]. At this time, the score clearly shows the user’s preference for each item. If these different preferences in the abstract sense are extracted (transmissible information, information resource value, etc.) and then represented concretely, it can be clearly seen that different items selected by different users can be connected to form a network of relationships. Let the obtained resource value flow in these relationships. When the target user chosen assigns the resource value obtained by the unselected user to the user’s unselected item, the target user has the unselected item of the recommended user, and the recommendation process is formed [14]. Multinode information resource recommendation process of bipartite graph network structure is shown in Figure 1.Figure 1
Multinode information resource recommendation processes of the bipartite graph network structure. (a) Assign initial resource value. (b) First step resource allocations. (c) The second step resource allocations.As shown in Figure1, assuming that the target user is u1, the multinode information resource allocation process of the bipartite graph is as follows:(1)
Set initial resource values for information data items. Set “1” as the initial resource value of the item selected by the target useru1 in the type L node information, and the item that is not selected is 2. The project set with the initial resource value of 1 is S=l1,l3.(2)
According to the selected relationship, the resource value assigned to theS set is integrated and distributed to the U adjacent nodes, so that the user set P=u1,u2,u3 can obtain the resource value. The resource value obtained by all users in user set P is the sum of the resource values of all project nodes l in set S divided by the median of node l.(3)
According to the selected relationship, first integrate the recommended resource values in theP sets, then assign the integrated user node resource values to the adjacent category l item nodes in the P sets, and finally get the information item. All items that are not selected will be included in the resource value. In this case, the obtained type l information node resource value includes the sum of all users’ information item resource values u in the user set P minus l information node degrees.For the resource diffusion process proposed by the multinode information resource allocation algorithm based on the bipartite graph network structure, after setting the target user asu, the specific steps are as follows:(1)
The initial resource value is determined, expressed by the following formula:(5)au,l=1,if useruchoose item l,u∈Uandl∈L,2,otherwise.Among them, the information iteml corresponding to the user u is the set initial resource value represented by αu,l.(2)
Select any userv from the P sets that has not selected project resources as the selected target user u; the process is as follows:(6)rv=∑l∈Lau,lavlαl,V∈U.Among them,Dl represents how many users have selected information item l, that is, the degree of this information item.(3)
The process of obtaining the resource of any itemx in the P set is expressed by the following formula:(7)rx=∑v∈Urvav,xdv.Among them,rx is the recommended resource value generated by item x when target user u recommends item x. The number of items dv selected by any user v is the degree of any user v.


## 2.1.1. Information Classification Processing Based on Collaborative Filtering Algorithm

Use preprocessing technology and data mining technology to process and classify the information resources in the node [6], and then build a scoring data model. Generally choose to use M×N scoring matrixR:(1)Rm,n=r1r2⋯rnr11r12⋯r1n⋮⋮⋱⋮rm1rm2⋯rmn.Among them,M represents the number of rows of the matrix, N represents the number of columns in the matrix, and the evaluation value of item j by data i is represented by Rij. An integer between 0 and 5 represents the data evaluation value. As the score of the data onto the item, the larger the value, the higher the popularity of the data, and 0 means that the data is not evaluated. You can also use an integer interval to indicate the evaluation level; the higher the level, the more popular it is.Use the constructed scoring data model to search the network information data and obtain the scoring vector of the scoring information on the network information data. The score vector is calculated according to the similarity formula, and the score similaritysimu,uj between data u and data uj in the data scoring matrix is obtained. Then, sort the obtained node information similarity in descending order, and we can get simu,u1>simu,u2,>⋯>simu,ux. Then, the size of the nearest neighbor value is determined according to the selection strategy. There are two main forms of selecting neighbor strategies. The first is to set the number of nearest neighbors to k in advance, and the second is to set a fixed similarity threshold. If the target similarity is greater than the threshold, the corresponding data can be divided into the target’s nearest neighbor set. However, long-term experience has proved that the method of determining the nearest neighbor value of a fixed value k is better than the latter. Therefore, a fixed number of neighbors of k are generally adopted to confirm the number of most similar neighbors. The nearest neighbor set can be expressed as KNNu=u1,u2,…uk. u∉KNNu,k∈1,m.We use k-means here [7]. Of course, there are now many evolved versions of the k-means algorithm, such as k-means ++ [8], and we will use the original k-means algorithm here because it has many advantages, for example, the algorithm guarantees better scalability when dealing with large datasets and the algorithm is of low complexity.


## 2.1.2. Similarity Calculation

In the collaborative filtering algorithm, the calculation of the nearest similarity is an important prerequisite for finding the neighbor set [9]. The similarity calculation function is divided into cosine similarity, modified cosine similarity, and correlation similarity [10]. Among them, the cosine similarity is to calculate the cosine value of the inner product angle between the network node information and the item space vector and this cosine value is used to calculate the similarity between the data [11]; the process is as follows:(2)simu,ν=cosu¯,ν¯=u¯,ν¯u¯||ν¯=∑i∈Iuνruirνi∑i∈Iuνrui2∑i∈Iuνrvi2.The purpose of adjusting the cosine similarity is to overcome the problem of different scoring scales caused by the influence of individual factors in the evaluation of network information data. The modification on the cosine similarity formula is mainly to calculate the cosine of the included angle after subtracting the obtained cosine similarity vector from the average score vector of the information data. The process is as follows:(3)simu,v=∑i∈Iuνrui−ru¯rvi−rv¯∑i∈Iurui−ru¯2∑i∈Iνrvi−rv¯2.Correlation similarity is also called Pearson’s coefficient. First, find the set of items whose information data has been scored jointly, and then calculate the correlation coefficient of the vector in the set of common score items to express the similarity in different network information data, as shown in the following formula:(4)sim=∑i∈Iuirui−ru¯rvi−rv¯∑i∈Iuvrui−ru¯2∑i∈Iuvrvi−rv¯2.In the above three similarity calculation formulas,Iu, Iv are the scored item sets of network information data u, v, Iu, Iv, and Iu, Iv are the number of elements, and the intersection of the scored item sets is Iuv=IU∩Iv, ru¯=∑i∈Iurui/Iu, rv¯=∑i∈Ivrvi/Iv, and uv, which is the average rating of the evaluation item. In fact, these three similarity calculation methods are calculating the similarity in the scores of the data. If the volume of the information data is not large, there will be related problems that will affect the accuracy of the calculation. Therefore, different similarity calculations should be selected according to different datasets. Method to ensure the accuracy of the calculation.After the node information is classified based on the collaborative filtering algorithm, the bipartite graph network structure is used to allocate the resources of the classified node information and recommend the information resources [12]. Suppose a bipartite graph network is represented by G=V,E, U represents user categories of classified information, and L represents item categories of classified information. There are U types of data information nodes and L types of data information nodes and the data information edge set between them. E forms a network structure. If it is assumed that there are N users L=l1,l2,…lN and M items U=u1,u2,…uM in this network structure, then a selection relationship will be formed into users and items, and an adjacency matrix A=αulM,N can be used to represent this relationship. If the user selects an item u, then αu,i=1; if not, αul=2.Every user has their own hobbies, so according to the items selected by the user, we can accurately understand the user’s likes and dislikes for certain items at this stage [13]. At this time, the score clearly shows the user’s preference for each item. If these different preferences in the abstract sense are extracted (transmissible information, information resource value, etc.) and then represented concretely, it can be clearly seen that different items selected by different users can be connected to form a network of relationships. Let the obtained resource value flow in these relationships. When the target user chosen assigns the resource value obtained by the unselected user to the user’s unselected item, the target user has the unselected item of the recommended user, and the recommendation process is formed [14]. Multinode information resource recommendation process of bipartite graph network structure is shown in Figure 1.Figure 1
Multinode information resource recommendation processes of the bipartite graph network structure. (a) Assign initial resource value. (b) First step resource allocations. (c) The second step resource allocations.As shown in Figure1, assuming that the target user is u1, the multinode information resource allocation process of the bipartite graph is as follows:(1)
Set initial resource values for information data items. Set “1” as the initial resource value of the item selected by the target useru1 in the type L node information, and the item that is not selected is 2. The project set with the initial resource value of 1 is S=l1,l3.(2)
According to the selected relationship, the resource value assigned to theS set is integrated and distributed to the U adjacent nodes, so that the user set P=u1,u2,u3 can obtain the resource value. The resource value obtained by all users in user set P is the sum of the resource values of all project nodes l in set S divided by the median of node l.(3)
According to the selected relationship, first integrate the recommended resource values in theP sets, then assign the integrated user node resource values to the adjacent category l item nodes in the P sets, and finally get the information item. All items that are not selected will be included in the resource value. In this case, the obtained type l information node resource value includes the sum of all users’ information item resource values u in the user set P minus l information node degrees.For the resource diffusion process proposed by the multinode information resource allocation algorithm based on the bipartite graph network structure, after setting the target user asu, the specific steps are as follows:(1)
The initial resource value is determined, expressed by the following formula:(5)au,l=1,if useruchoose item l,u∈Uandl∈L,2,otherwise.Among them, the information iteml corresponding to the user u is the set initial resource value represented by αu,l.(2)
Select any userv from the P sets that has not selected project resources as the selected target user u; the process is as follows:(6)rv=∑l∈Lau,lavlαl,V∈U.Among them,Dl represents how many users have selected information item l, that is, the degree of this information item.(3)
The process of obtaining the resource of any itemx in the P set is expressed by the following formula:(7)rx=∑v∈Urvav,xdv.Among them,rx is the recommended resource value generated by item x when target user u recommends item x. The number of items dv selected by any user v is the degree of any user v.


## 3. Recommended Methods of Physical Education Online Course Resources

At present, there are some problems in the teaching of physical education in colleges and universities, such as shortage of teachers, limited time, insufficient expansion of teaching content, and “contradictions between learning and training.” Therefore, it is necessary to construct and apply digital teaching resources such as physical education courses and network courses, develop network teaching of physical education theory courses, improve teaching methods, and improve teaching effects. Through the abovementioned collaborative filtering recommendation algorithm, the physical education teaching network course resource recommendation is realized.


### 3.1. Analysis of the Importance of the Recommendation of Physical Education Online Course Resources

The purpose of the design of physical education network resources is to “guide students to perform scientific physical exercises.” It is necessary to design the overall structure of the content of network resources. The content must be systematic. It should include basic sports knowledge, physical exercise methods, health care and rehabilitation, exercise prescriptions, information feedback, actual combat exercises, pictures, videos, and expert answers. The design of the physical education network resource system should be different from other network curriculum teaching designs. It should be based on “instructing students to learn and practice” and let students learn “discovery” and exercise consciously. It can be achieved by setting up actual combat simulation area, exercise effect feedback, and sports experts online. The actual combat simulation area is mainly based on the actual combat exercise simulation set up according to physical exercise needs. The system can provide immediate guidance information according to the students’ problems, difficulties, and key points during the actual combat exercise. The effect feedback area is a space for exercisers to feedback the effects, difficulties, problems, and so on of their physical exercises in the system. After receiving feedback from exercisers, the system will immediately evaluate, feedback, encourage, praise, guide, suggest, and so on and prescribe exercise. Sports experts online, e-mail, and so on are spaces to strengthen the interaction between teachers and students. The integration of online sports teaching information resources is a systematic project. The research on the integration of sports information resources is optimized and integrated based on scientific theories and methods to form a coconstructed and shared virtual sports information resource system. This system is conducive to enriching the theory of sports information resource integration and accelerating the construction of campus sports digital libraries; it is conducive to the orderly development of sports information resources and the increase in utilization rate; it helps to eliminate information islands, achieve the greatest degree of sports resource sharing, and provide a theoretical basis for realizing the domestic sports information network system, the construction of sports information value-added services, and the simultaneous development of the market economy.


### 3.2. Information Similarity Recommendation Process under Collaborative Algorithm

According to the current information data type of the sports resource library, it is applied to the recommendation model. Taking into account the real-time status of the resource-sharing website, the collaborative filtering algorithm will be applied to recommend the information about the sports resource library in real time to ensure the recommendation quality and improve the sparseness of the information [15].


#### 3.2.1. Scoring of Nondataset Items

By finding the neighboring targets of the data information, predict the rating of the intended users on the items of the nondataset:(1)
Score the same data for useri and user j, set this item set to RIi,j, and the relational expression is(8)RIi,j=RIi∪RIj.The formula represents the set of rating feedbacks on the corresponding usersi and j in the same dataset.(2)
In the datasetRIi,j of user i, the target item NRIi without feedback score can be expressed as(9)NRIi=RIi,j−RIi.(3)
Gradually calculatei user’s feedback score for each type of target item in the data item set NRIi. First of all, assuming that the feedback score of k items will be calculated in the next step, it needs to be based on user i’s score on k items and compare it with other target items. The similarity is expressed as simk,j; then, the similarity measures [16] can be expressed based on the cosine similarity function [17].(4)
According to the similarity with the information, perform the descending combination arrangement, and select the most similar dataset with the item’s neighboring threshold ofSI as NIk.(5)
Taking useri as an example, it is predicted that its score of item collection k is expressed as Pi,k, and the calculation formula is as follows:(10)Pi,k=Pk¯+∑n∈NIksimk,n⋅Ri,n−Rn¯∑n∈NIksimk,n.In the formula,Pk¯ and Rn¯ represent the average of the predicted scores of all user item sets k and n, respectively.(6)
According to the above steps, the loop calculation is performed in sequence, and the predicted feedback scores of all users for all the project collections can be obtained in the same way. SetRIi,j items corresponding to user i and user j, and use the similarity threshold [18] correlation SI to meet this condition k∈RIi,j so that the actual score of sports resource information database k is(11)Ri,kri,k,pi,k.


#### 3.2.2. Remaining Project Prediction Score

(1)
Calculate the data similarity between usersi and j, respectively, and use the above cosine function to calculate:(12)simk,j=∑c∈UIi,jRi,c−Ri⋅Rj,c−Rj¯∑c∈IiRi,c−Ri¯2⋅∑c∈IjRj,c−Rj¯2.In the formula,UIi,j represents the intersection between user ratings i and j, represented as UIi,j=RIi∩RIj, Pi¯ and Rj¯ represent the average of the actual ratings of all user item sets k and n, respectively. Assuming that i users only give feedback scores to the sports information of No. 1, No. 3, and No. 5 in the resource library, it is expressed as Ri¯=5+4+3/3=4.(2)
According to the above process, similarly calculate the information recommendation similarity in the remainingm−i user in turn, according to the similarity in the information and arrange them in descending order to establish a data collection, that is, to build a large-scale user set of NUIi=U1,U2,…,Um−1 with the overall user area as the target. And let i∉NUIi because the recommendation information similarity in Ui and i is both the highest, followed by U2, so it is necessary to combine and arrange sequentially, design the information resource similarity in Um−1 to be the lowest, and set the most similar users. The maximum threshold between the two is set to KU. In this NUf collection, the user whose threshold number is KU is selected, and this is regarded as the nearest neighbor collection RUi of the i users.(3)
Use the nearest neighbor data collectionRUi in step (2), and then detect the actual scores of the sports resource database information k that has not been feedback scored in all the remaining data collections:(13)Ri,k=Rj¯+∑n∈RUisimn,i⋅Rn,k−Rn¯∑n∈RUisimi,n.After completing the resource library information recommendation and repeating the above steps in turn, all users’ predicted score and actual score values can be calculated.


#### 3.2.3. Implementation of Final Sports Resource Database Information Recommendation

First, select the resource sequence that needs to be recommended and use two methods to recommend it in resource library data effectively. The first method is to compare the scores of all predicted project datasets with the maximum threshold ofKU. For example, the recommendation list is sent to the intended customers and then combined and arranged in the form of numerical value according to the magnitude of the predicted score value, and the project resource library information on the larger score value is selected and added to the user’s recommendation list. Finally, according to the above steps, the similarity value calculated by the collaborative filtering algorithm is used to distinguish the similarity between the resource information databases, realize the optimal data selection, and complete the physical education network course resource recommendation.


## 3.1. Analysis of the Importance of the Recommendation of Physical Education Online Course Resources

The purpose of the design of physical education network resources is to “guide students to perform scientific physical exercises.” It is necessary to design the overall structure of the content of network resources. The content must be systematic. It should include basic sports knowledge, physical exercise methods, health care and rehabilitation, exercise prescriptions, information feedback, actual combat exercises, pictures, videos, and expert answers. The design of the physical education network resource system should be different from other network curriculum teaching designs. It should be based on “instructing students to learn and practice” and let students learn “discovery” and exercise consciously. It can be achieved by setting up actual combat simulation area, exercise effect feedback, and sports experts online. The actual combat simulation area is mainly based on the actual combat exercise simulation set up according to physical exercise needs. The system can provide immediate guidance information according to the students’ problems, difficulties, and key points during the actual combat exercise. The effect feedback area is a space for exercisers to feedback the effects, difficulties, problems, and so on of their physical exercises in the system. After receiving feedback from exercisers, the system will immediately evaluate, feedback, encourage, praise, guide, suggest, and so on and prescribe exercise. Sports experts online, e-mail, and so on are spaces to strengthen the interaction between teachers and students. The integration of online sports teaching information resources is a systematic project. The research on the integration of sports information resources is optimized and integrated based on scientific theories and methods to form a coconstructed and shared virtual sports information resource system. This system is conducive to enriching the theory of sports information resource integration and accelerating the construction of campus sports digital libraries; it is conducive to the orderly development of sports information resources and the increase in utilization rate; it helps to eliminate information islands, achieve the greatest degree of sports resource sharing, and provide a theoretical basis for realizing the domestic sports information network system, the construction of sports information value-added services, and the simultaneous development of the market economy.


## 3.2. Information Similarity Recommendation Process under Collaborative Algorithm

According to the current information data type of the sports resource library, it is applied to the recommendation model. Taking into account the real-time status of the resource-sharing website, the collaborative filtering algorithm will be applied to recommend the information about the sports resource library in real time to ensure the recommendation quality and improve the sparseness of the information [15].


### 3.2.1. Scoring of Nondataset Items

By finding the neighboring targets of the data information, predict the rating of the intended users on the items of the nondataset:(1)
Score the same data for useri and user j, set this item set to RIi,j, and the relational expression is(8)RIi,j=RIi∪RIj.The formula represents the set of rating feedbacks on the corresponding usersi and j in the same dataset.(2)
In the datasetRIi,j of user i, the target item NRIi without feedback score can be expressed as(9)NRIi=RIi,j−RIi.(3)
Gradually calculatei user’s feedback score for each type of target item in the data item set NRIi. First of all, assuming that the feedback score of k items will be calculated in the next step, it needs to be based on user i’s score on k items and compare it with other target items. The similarity is expressed as simk,j; then, the similarity measures [16] can be expressed based on the cosine similarity function [17].(4)
According to the similarity with the information, perform the descending combination arrangement, and select the most similar dataset with the item’s neighboring threshold ofSI as NIk.(5)
Taking useri as an example, it is predicted that its score of item collection k is expressed as Pi,k, and the calculation formula is as follows:(10)Pi,k=Pk¯+∑n∈NIksimk,n⋅Ri,n−Rn¯∑n∈NIksimk,n.In the formula,Pk¯ and Rn¯ represent the average of the predicted scores of all user item sets k and n, respectively.(6)
According to the above steps, the loop calculation is performed in sequence, and the predicted feedback scores of all users for all the project collections can be obtained in the same way. SetRIi,j items corresponding to user i and user j, and use the similarity threshold [18] correlation SI to meet this condition k∈RIi,j so that the actual score of sports resource information database k is(11)Ri,kri,k,pi,k.


### 3.2.2. Remaining Project Prediction Score

(1)
Calculate the data similarity between usersi and j, respectively, and use the above cosine function to calculate:(12)simk,j=∑c∈UIi,jRi,c−Ri⋅Rj,c−Rj¯∑c∈IiRi,c−Ri¯2⋅∑c∈IjRj,c−Rj¯2.In the formula,UIi,j represents the intersection between user ratings i and j, represented as UIi,j=RIi∩RIj, Pi¯ and Rj¯ represent the average of the actual ratings of all user item sets k and n, respectively. Assuming that i users only give feedback scores to the sports information of No. 1, No. 3, and No. 5 in the resource library, it is expressed as Ri¯=5+4+3/3=4.(2)
According to the above process, similarly calculate the information recommendation similarity in the remainingm−i user in turn, according to the similarity in the information and arrange them in descending order to establish a data collection, that is, to build a large-scale user set of NUIi=U1,U2,…,Um−1 with the overall user area as the target. And let i∉NUIi because the recommendation information similarity in Ui and i is both the highest, followed by U2, so it is necessary to combine and arrange sequentially, design the information resource similarity in Um−1 to be the lowest, and set the most similar users. The maximum threshold between the two is set to KU. In this NUf collection, the user whose threshold number is KU is selected, and this is regarded as the nearest neighbor collection RUi of the i users.(3)
Use the nearest neighbor data collectionRUi in step (2), and then detect the actual scores of the sports resource database information k that has not been feedback scored in all the remaining data collections:(13)Ri,k=Rj¯+∑n∈RUisimn,i⋅Rn,k−Rn¯∑n∈RUisimi,n.After completing the resource library information recommendation and repeating the above steps in turn, all users’ predicted score and actual score values can be calculated.


### 3.2.3. Implementation of Final Sports Resource Database Information Recommendation

First, select the resource sequence that needs to be recommended and use two methods to recommend it in resource library data effectively. The first method is to compare the scores of all predicted project datasets with the maximum threshold ofKU. For example, the recommendation list is sent to the intended customers and then combined and arranged in the form of numerical value according to the magnitude of the predicted score value, and the project resource library information on the larger score value is selected and added to the user’s recommendation list. Finally, according to the above steps, the similarity value calculated by the collaborative filtering algorithm is used to distinguish the similarity between the resource information databases, realize the optimal data selection, and complete the physical education network course resource recommendation.


## 3.2.1. Scoring of Nondataset Items

By finding the neighboring targets of the data information, predict the rating of the intended users on the items of the nondataset:(1)
Score the same data for useri and user j, set this item set to RIi,j, and the relational expression is(8)RIi,j=RIi∪RIj.The formula represents the set of rating feedbacks on the corresponding usersi and j in the same dataset.(2)
In the datasetRIi,j of user i, the target item NRIi without feedback score can be expressed as(9)NRIi=RIi,j−RIi.(3)
Gradually calculatei user’s feedback score for each type of target item in the data item set NRIi. First of all, assuming that the feedback score of k items will be calculated in the next step, it needs to be based on user i’s score on k items and compare it with other target items. The similarity is expressed as simk,j; then, the similarity measures [16] can be expressed based on the cosine similarity function [17].(4)
According to the similarity with the information, perform the descending combination arrangement, and select the most similar dataset with the item’s neighboring threshold ofSI as NIk.(5)
Taking useri as an example, it is predicted that its score of item collection k is expressed as Pi,k, and the calculation formula is as follows:(10)Pi,k=Pk¯+∑n∈NIksimk,n⋅Ri,n−Rn¯∑n∈NIksimk,n.In the formula,Pk¯ and Rn¯ represent the average of the predicted scores of all user item sets k and n, respectively.(6)
According to the above steps, the loop calculation is performed in sequence, and the predicted feedback scores of all users for all the project collections can be obtained in the same way. SetRIi,j items corresponding to user i and user j, and use the similarity threshold [18] correlation SI to meet this condition k∈RIi,j so that the actual score of sports resource information database k is(11)Ri,kri,k,pi,k.


## 3.2.2. Remaining Project Prediction Score

(1)
Calculate the data similarity between usersi and j, respectively, and use the above cosine function to calculate:(12)simk,j=∑c∈UIi,jRi,c−Ri⋅Rj,c−Rj¯∑c∈IiRi,c−Ri¯2⋅∑c∈IjRj,c−Rj¯2.In the formula,UIi,j represents the intersection between user ratings i and j, represented as UIi,j=RIi∩RIj, Pi¯ and Rj¯ represent the average of the actual ratings of all user item sets k and n, respectively. Assuming that i users only give feedback scores to the sports information of No. 1, No. 3, and No. 5 in the resource library, it is expressed as Ri¯=5+4+3/3=4.(2)
According to the above process, similarly calculate the information recommendation similarity in the remainingm−i user in turn, according to the similarity in the information and arrange them in descending order to establish a data collection, that is, to build a large-scale user set of NUIi=U1,U2,…,Um−1 with the overall user area as the target. And let i∉NUIi because the recommendation information similarity in Ui and i is both the highest, followed by U2, so it is necessary to combine and arrange sequentially, design the information resource similarity in Um−1 to be the lowest, and set the most similar users. The maximum threshold between the two is set to KU. In this NUf collection, the user whose threshold number is KU is selected, and this is regarded as the nearest neighbor collection RUi of the i users.(3)
Use the nearest neighbor data collectionRUi in step (2), and then detect the actual scores of the sports resource database information k that has not been feedback scored in all the remaining data collections:(13)Ri,k=Rj¯+∑n∈RUisimn,i⋅Rn,k−Rn¯∑n∈RUisimi,n.After completing the resource library information recommendation and repeating the above steps in turn, all users’ predicted score and actual score values can be calculated.


## 3.2.3. Implementation of Final Sports Resource Database Information Recommendation

First, select the resource sequence that needs to be recommended and use two methods to recommend it in resource library data effectively. The first method is to compare the scores of all predicted project datasets with the maximum threshold ofKU. For example, the recommendation list is sent to the intended customers and then combined and arranged in the form of numerical value according to the magnitude of the predicted score value, and the project resource library information on the larger score value is selected and added to the user’s recommendation list. Finally, according to the above steps, the similarity value calculated by the collaborative filtering algorithm is used to distinguish the similarity between the resource information databases, realize the optimal data selection, and complete the physical education network course resource recommendation.


## 4. Experimental Results and Analysis

### 4.1. Test Preparation

The computer with the operating system Ubuntu Kylin 16.04 LTS is selected; combined with the characteristics of the physical education network curriculum resources, the resource data is processed into the physical education network curriculum resource dataset in a dichotomy method. We divide the dataset into a training dataset, a test dataset, in a ratio of 3 : 1, and use cross-validation methods to prevent overfitting of the model. Then, we use the matrix factorization technique to process the training set as a numerical root mean square. The processing process can be expressed as(14)RM=∑u,i∈Cru−ri2C.Among them,RM represents the numerical root mean square, ru, ri represent the training matrix of the training set and the test set, and C represents the number of datasets. After decomposing into the numerical root mean square value, the matching score between the user and the resource of the physical education network course resource is evaluated according to the numerical root mean square. Using literature [4]’s method and literature [5]’s method as experimental comparison methods, test the application performance of the three methods of the recommendation of physical education network course resources.


### 4.2. Test Results and Analysis

Reduce the dimension of the physical education network curriculum resource dataset into a dataset to be labeled, calculate the similarity between the datasets, control the three recommendation algorithms to process the same similarity value, and define the physical education network curriculum resource recommendation process. Calculate the size of the parameters generated during the recommendation process of the three recommendation algorithms. The numerical relationship can be expressed as(15)TM=∑u,i∈Cru−ri2RM.Among them,TM represents the calculated value, and the meaning of the other parameters remains unchanged. Corresponding to the parameter values calculated above, the three recommendation algorithms are controlled to recommend 30 positions. After the recommendation algorithms of the three physical education network course resource recommendation algorithms are completed, the TM parameter sizes of the three recommendation algorithms are compared, and the result is shown in Figure 2.Figure 2
TM parameter obtained by three recommended algorithms.Corresponding to the definedTM parameter values relationship, control the three recommendation algorithms to recommend positions of the same parameters, and define that the calculated TM parameter value is close to the value TM, which means the higher quality of the physical education network course post recommended by this recommendation algorithm. According to the numerical change results shown in Figure 2, the TM parameter value of the algorithm in [4] is about 0.9607, and the recommendation quality of this recommendation algorithm is the worst. The TM parameter of the algorithm in [5] is around 0.9650, and the recommendation quality of this recommendation algorithm is not good. The TM parameter obtained by the designed recommendation algorithm is around 0.9700. Compared with the two selected recommendation algorithms, the TM parameter value obtained by the designed recommendation algorithm is the largest, and the recommendation quality of the algorithm is the highest. It can be seen from Figure 2 that the fluctuation trend of the three methods is basically the same, which proves the credibility of our method. In addition, our method is significantly higher than [4] and [5]. It shows the application effect of our method in PE online course resource recommendation.In the above experimental environment, theTM parameter label obtained by the above calculation is processed into the physical education network curriculum resource dataset, the marked training dataset is called, and the host computer task manager running three physical education network curriculum resource recommendation algorithms is called. When the algorithm processes 400 sets of training data, it takes time to count the three recommendation algorithms. The time-consuming results are shown in Figure 3.Figure 3
Time-consuming results of three recommendation algorithms.After calling the task manager of the host computer that runs the recommendation algorithm, gradually increase the number of training datasets for the recommendation algorithm. According to the experimental results, the algorithm in [4] consumes 0.8 s when processing 400 sets of training sets, and the running time required for the recommendation algorithm is longer. According to [5], when the algorithm processes 400 sets of data, the algorithm takes 0.7 s, and the actual running time is longer. When the designed recommendation algorithm processes a dataset of the same size, the algorithm recommendation time is 0.65 s. Compared with the two selected recommendation algorithms, the designed recommendation algorithm consumes the shortest time. The accuracy rate of physical education teaching online courses resources recommendation is shown in Figure 4. As the number of guesses increases, the time consumption of all algorithms presents an increasing trend, but the time consumption of our method is significantly lower than that of the comparison method. From the perspective of algorithm time, the effectiveness of our method is proved.Figure 4
The accuracy rate of physical education teaching online courses resources recommendation.It can be clearly seen from the comparison curve of the algorithms in Figure4 that the average accuracy of using the algorithm in this paper to recommend physical education online course resources is 97.5%, which is better than the other two algorithms. Because this article fully considers the relationship between user interest and project resources and shields outdated or low-interest score data, this will make the quality of the recommendation results even better, and the data collection and update will be improved. There is also a great improvement. The other two methods do not fully consider the issue of personalized services, leading to a downward trend in their effect curve. The timeliness of the information and the efficiency of information recommendations are also low, affecting the overall recommendation quality. Moreover, the method proposed in this paper is more stable, and there are obvious fluctuations in the experimental results of [4, 5]. It is superior to the other two algorithms in stability. In addition, because we screened some low-score data, the recommendation results achieved better results. The other two methods do not fully consider the problem of personalized service, which affects the overall recommendation quality.


## 4.1. Test Preparation

The computer with the operating system Ubuntu Kylin 16.04 LTS is selected; combined with the characteristics of the physical education network curriculum resources, the resource data is processed into the physical education network curriculum resource dataset in a dichotomy method. We divide the dataset into a training dataset, a test dataset, in a ratio of 3 : 1, and use cross-validation methods to prevent overfitting of the model. Then, we use the matrix factorization technique to process the training set as a numerical root mean square. The processing process can be expressed as(14)RM=∑u,i∈Cru−ri2C.Among them,RM represents the numerical root mean square, ru, ri represent the training matrix of the training set and the test set, and C represents the number of datasets. After decomposing into the numerical root mean square value, the matching score between the user and the resource of the physical education network course resource is evaluated according to the numerical root mean square. Using literature [4]’s method and literature [5]’s method as experimental comparison methods, test the application performance of the three methods of the recommendation of physical education network course resources.


## 4.2. Test Results and Analysis

Reduce the dimension of the physical education network curriculum resource dataset into a dataset to be labeled, calculate the similarity between the datasets, control the three recommendation algorithms to process the same similarity value, and define the physical education network curriculum resource recommendation process. Calculate the size of the parameters generated during the recommendation process of the three recommendation algorithms. The numerical relationship can be expressed as(15)TM=∑u,i∈Cru−ri2RM.Among them,TM represents the calculated value, and the meaning of the other parameters remains unchanged. Corresponding to the parameter values calculated above, the three recommendation algorithms are controlled to recommend 30 positions. After the recommendation algorithms of the three physical education network course resource recommendation algorithms are completed, the TM parameter sizes of the three recommendation algorithms are compared, and the result is shown in Figure 2.Figure 2
TM parameter obtained by three recommended algorithms.Corresponding to the definedTM parameter values relationship, control the three recommendation algorithms to recommend positions of the same parameters, and define that the calculated TM parameter value is close to the value TM, which means the higher quality of the physical education network course post recommended by this recommendation algorithm. According to the numerical change results shown in Figure 2, the TM parameter value of the algorithm in [4] is about 0.9607, and the recommendation quality of this recommendation algorithm is the worst. The TM parameter of the algorithm in [5] is around 0.9650, and the recommendation quality of this recommendation algorithm is not good. The TM parameter obtained by the designed recommendation algorithm is around 0.9700. Compared with the two selected recommendation algorithms, the TM parameter value obtained by the designed recommendation algorithm is the largest, and the recommendation quality of the algorithm is the highest. It can be seen from Figure 2 that the fluctuation trend of the three methods is basically the same, which proves the credibility of our method. In addition, our method is significantly higher than [4] and [5]. It shows the application effect of our method in PE online course resource recommendation.In the above experimental environment, theTM parameter label obtained by the above calculation is processed into the physical education network curriculum resource dataset, the marked training dataset is called, and the host computer task manager running three physical education network curriculum resource recommendation algorithms is called. When the algorithm processes 400 sets of training data, it takes time to count the three recommendation algorithms. The time-consuming results are shown in Figure 3.Figure 3
Time-consuming results of three recommendation algorithms.After calling the task manager of the host computer that runs the recommendation algorithm, gradually increase the number of training datasets for the recommendation algorithm. According to the experimental results, the algorithm in [4] consumes 0.8 s when processing 400 sets of training sets, and the running time required for the recommendation algorithm is longer. According to [5], when the algorithm processes 400 sets of data, the algorithm takes 0.7 s, and the actual running time is longer. When the designed recommendation algorithm processes a dataset of the same size, the algorithm recommendation time is 0.65 s. Compared with the two selected recommendation algorithms, the designed recommendation algorithm consumes the shortest time. The accuracy rate of physical education teaching online courses resources recommendation is shown in Figure 4. As the number of guesses increases, the time consumption of all algorithms presents an increasing trend, but the time consumption of our method is significantly lower than that of the comparison method. From the perspective of algorithm time, the effectiveness of our method is proved.Figure 4
The accuracy rate of physical education teaching online courses resources recommendation.It can be clearly seen from the comparison curve of the algorithms in Figure4 that the average accuracy of using the algorithm in this paper to recommend physical education online course resources is 97.5%, which is better than the other two algorithms. Because this article fully considers the relationship between user interest and project resources and shields outdated or low-interest score data, this will make the quality of the recommendation results even better, and the data collection and update will be improved. There is also a great improvement. The other two methods do not fully consider the issue of personalized services, leading to a downward trend in their effect curve. The timeliness of the information and the efficiency of information recommendations are also low, affecting the overall recommendation quality. Moreover, the method proposed in this paper is more stable, and there are obvious fluctuations in the experimental results of [4, 5]. It is superior to the other two algorithms in stability. In addition, because we screened some low-score data, the recommendation results achieved better results. The other two methods do not fully consider the problem of personalized service, which affects the overall recommendation quality.


## 5. Conclusion

This paper proposes a method for recommending physical education online course resources based on machine learning algorithms, recommends physical education online course resources based on collaborative filtering algorithms, and draws the following conclusions through experimental analysis:(1)
Through the steps of machine learning and clustering algorithms, the sparse resource problem caused by the original data is effectively improved, and the accuracy of the recommendation algorithm is improved.(2)
Dataset similarity calculation based on the collaborative filtering model algorithm, by comparing the way users achieve feedback scoring on the data, effectively distinguishing similar data, and efficiently recommending physical education online course resources.(3)
The recommendation method of physical education network course resources based on machine learning algorithms has clear goals, high overall efficiency, and high accuracy.



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*Source: 4925605-2021-10-31.xml*