# A Method for Recommending Sports Network Course Resources Based on Collaborative Filtering Technology

Authors: Xu Bin
Journal: Security and Communication Networks
 (2021)

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## Abstract

In the process of online course resource recommendation, the output of recommendation results is often unstable. Therefore, a physical education online course resource recommendation method based on collaborative filtering technology is proposed. Firstly, the learning preference of e-learners is calculated, the frequency index of the word frequency-inverse document is defined, the correlation between courses is reflected, and the specific needs of students for PE online course resource recommendation are understood. Then, the collaborative filtering recommendation algorithm is used to generate the similarity matrix and correlation matrix, update the edge characteristics of sports online curriculum resources, collect and refine the hidden index of sports online curriculum resources, optimize the prediction rules of the neighborhood of the most similar teaching unit, and complete the recommendation of sports online curriculum resources. Experimental results show that, for 1000 keywords, the method has the highest single average matching degree, the recommendation process is stable, and theF1 value is more than 0.9, and the practical application is ensured.


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## Body

## 1. Introduction

Online course support platform can generally support thousands of courses and support the creation of the unlimited curriculum directory. Course administrators can create, move, download, and modify courses at any time, set courses as active or hidden, set course progress, set flexible permissions and levels for courses, and allow students to log out of courses. Network course support platform can not only support the digital learning of schools but also support the upload and download of resources, and resources can be classified into management and research. Teachers can easily upload and download their own resources. It also provides learning resources for registered students to upload and download, and a library of teacher and student learning resources can be opened for everyone to share. Many regions are trying to use the network curriculum support platform to carry out teachers’ educational technology ability and information technology ability in distance training and promote teachers’ professional development and have achieved preliminary results. While the advantages of the web support platform are demonstrated in distance training, it also has disadvantages, such as visualization of remote guidance and personalized recommendation of resources [1].In [2], aiming at the problem of cloud storage and personalized recommendation of IT teaching resources in the cloud computing environment, this paper puts forward a management method of IT teaching resources based on the distributed file system and user interest, solves the problem of cloud storage of network teaching resources, introduces the student scoring matrix, analyzes the similarity of student-course attributes, and improves the quality and accuracy of recommendation. In [3], under the background of the new crown epidemic situation, in order to provide teachers with effective teaching feedback and analyze how to provide students with appropriate online education resources, this has become an urgent problem and challenge for online education. Around the intelligent adaptation scheme of online courses, a large data platform for students’ labeling shall be constructed, with emphasis on the analysis of user data classification, acquisition methods, and design methods of the complete student portrait system, and in combination with multiattribute decision models, the grading and classification methods of online courses shall be discussed so as to facilitate the development of more flexible and effective online course teaching according to the individual characteristics of learners. In [4], research on a comprehensive school physical activity plan consistent with the middle school health optimization physical education curriculum model, emphasizing the importance of a strong external support mechanism, needs assessments and teacher training to promote physical activity planning, which can provide critical support for school efforts to create and sustain development.At present, the biggest defect in the physical education of online education lies in the lack of teaching resources, such as multimedia courseware and teaching materials. Moreover, the quality of physical education courseware brought by schools is also low, which makes it difficult to guarantee the quality of physical education online teaching. Therefore, it is necessary to find suitable physical education resources from the network and recommend them to college teachers and students to achieve good physical education. Therefore, this paper puts forward a method of recommending teaching resources of sports online courses based on collaborative filtering technology. By using the collaborative filtering algorithm, the edge features of resources are associated with teaching needs, and the correlation analysis between them is enhanced, thus realizing the accurate recommendation of sports teaching resources.


## 2. Recommendation of the Teaching Unit of the Physical Education Network Course Based on Collaborative Filtering Technology

Based on the internet, the distance education of physical education breaks through the limitation of time and space, turns the closed teaching into the open teaching, and realizes the students’ self-study. This physical education model provides a good platform for the scientific, open, interactive, collaborative, and autonomous nature of the physical education network curriculum. The development of sports network courses embodies the new concept of sports including network technology in this good platform and makes it the basic concept of sports network course development. Therefore, it requires a new combination and classification of sports knowledge and sports skills in the form of courses by comprehensive use of network, multimedia, and other technologies as well as the knowledge of various courses such as pedagogy and psychology to construct the structure of network courses, to reflect the relevance of courses, to coordinate the relationship between teachers and students in sports teaching, to express the human activities in the process of sports teaching, and to make the system of sports network courses orderly and humane.Because the web-based course is mostly autonomous learning, the content of the web-based course should be able to adapt to learners’ knowledge level, and the content of the web-based course should be different for different learners.


### 2.1. Design Principles of Recommending Methods for Network Curriculum Resources

(1)
Principle of Individualization. According to the characteristics of students’ physical and mental development and the needs of individualized learning, the individualized characteristics of web-based teaching should be reflected in the course design style.(2)
Humanization Principle. Interface design should be highly humanized, provide students with a simple and practical navigation system function to facilitate students to browse and find the required learning content, facilitate teachers to conduct online physical guidance, knowledge review, and essay corrections for students, facilitate online communication, evaluation guidance and feedback between teachers and students, and meet the teaching needs of teachers and students to the greatest extent.(3)
Principle of Richness and Openness. In order to improve the teaching quality of online courses, it is necessary to design and develop a practical tool for online course teaching, so as to ensure that teachers can smoothly transfer rich teaching resources of physical fitness knowledge and skills to the online course platform, make public display on the online course platform, and set up real-time communication areas and comment areas to facilitate teachers and students to learn, communicate, and discuss in real time or analyze, count, sort out, and summarize the teaching.(4)
Principle of Practicability. The overall design of the PE network course should be student-centered and conform to the students’ cognitive law, interest in learning, and psychological characteristics in teaching content, method selection, and textbook preparation. For example, in addition to presenting a certain special fitness knowledge and skills in the form of text, image, sound, animation, video, and other multimedia forms, it is necessary to properly supplement such knowledge as targeted and practical healthcare, diet and healthcare, fitness and health, and prevention and treatment of sports injuries, so as to meet the psychological needs of students, broaden their vision, enrich their knowledge structure, and improve their comprehensive quality.


### 2.2. Collection and Refinement of Network Curriculum Resources for Physical Education

Since the appearance of large-scale open online courses, the development of online education is in full swing. More and more students begin to get used to this new and rich learning form. As the number of online course resources increases and the variety of courses becomes more and more diverse, students often encounter difficult choices when considering the courses they want to study. Because of the characteristics of online courses, such as less text information, less student behavior information, and lack of evaluation information, the traditional recommendation algorithm cannot be directly applied to the recommendation of online courses, so there is still a lot of room for related work. Usually, the function of recommendation algorithm is grading and forecasting, but in practice, students only pay attention to the first few results of recommendation, so the recommendation system is more to solve the ranking problem. The single recommendation algorithm condition is too limited to provide the best recommendation result for students. To sum up, it is necessary to collect and refine the network curriculum resources of physical education.Firstly, the text information of the course is abstracted into the topic vector, which includes the name and introduction of the course. Then, the subject feature vector is constructed according to the students’ information, but after studying the students’ information in the dataset, it is found that few students fill in the personal interest label. Therefore, it is difficult to use this feature as a source of data to obtain students’ preferences.Firstly, the keyword in a period of time is extracted and integrated to obtain a dictionaryW. If the vocabulary contained in it is n and a student u is known, the following expression is used to represent the student’s file vector [5]:(1)profileu=vuw1,vuw2,...,vuwn,wi∈W, and the weight of the 33rd keyword in the file vector of i and u students is vuwi, which is used to describe the preference of u students for wi.In the process of keyword processing, the evaluation indextfidf of vocabulary importance is word frequency-antidocument frequency. The more a word appears in the file, the stronger the importance is. The word frequency-antidocument frequency index is defined through the following methods:(2)Rtft,d=ft,dmaxfw,d:w∈d,Rfidt,D=lnDd∈D:t∈d,Rtfidft,d,D=tft,d×idft,D.The word is expressed ast, the document is d, the antidocument frequency of word t is Ridft,D, and the number of occurrences of word t in document d is the initial frequency ft,D.Similarly, word frequency antistudent frequency (i.e.,tfiuf) is used to evaluate students’ preference for keywords, and its definition formula is as follows:(3)Rftt,u=ft,dumaxfw,du:w∈du,(4)Ridft,U=lnUu∈U:t∈du,(5)Rtfiuft,u,U=Rtft,u×Ridft,U,where the social network content published by students is du, the student collection is U, the occurrence frequency of vocabulary in students’ social network content is Rtft,u, the antistudent frequency of vocabulary is Ridft,U, and the occurrence frequency of vocabulary in du is ft,du.The lexical weight of the student profile vector is solved, and the student preference similarity is evaluated through the cosine similarity between the vectors [6]. The following formula shows the membership function of vectors V1 and V2:(6)cosineV1,V2=V1⋅V2V1⋅V2.Thus, the expression of preference similarity between the following studentu and student a is derived:(7)simcu,a=cosineprofileu,profilea=∑i=1nvuwi⋅vawi∑i=1nvu2wi⋅∑i=1nva2wi.According to the preference similarity, the learning preferences of teaching units and online learners are obtained, so as to determine which teaching units can be recommended to learners.


### 2.3. Recommendation of Teaching Units of Physical Education Network Courses

Teaching unit recommendation is an online process. When learners interact with the system, they can collect its access history information and search for the attributes of teaching resources. When recommending new teaching resources to learners, check the teaching units visited by learners, confirm whether the new teaching units are in the vicinity of the most similar teaching units, predict the recommended resources, and recommend the teaching units with the largest similarity value. According to the above obtained recommendation dataFm, the correlation between prediction F1,F2,…,Fm and prediction rules are shown in Figure 1.Figure 1
Schematic diagram of prediction rules in neighbors of most similar teaching units.Based on Figure1, different prediction methods are set according to multiple prediction conditions, and the surface correlation and implicit correlation are grasped to obtain the detailed correlation degree value of differentiation [7]. Create a set R and make the set R=F1,F2,…,Fm. If the setting k1,k2,…,km is a prediction condition corresponding to the recommended data F1,F2,…,Fm, the prediction coefficient calculation expression is(8)Kn=ωm×gmFm∩gm−1Fm−1∑m=1nkm−ks,where Kn represents the set n prediction coefficients; ωm represents prediction constraints; gmFm represents m predictions of recommended data Fm; gm−1Fm−1 represents m−1 predictions for the prediction data Fm; and ks represents the adjustment index under the influence of prediction intensity s at each prediction [8, 9]. Predict the correlation degree of the recommended data according to the calculated prediction coefficient. When the result is positive, it indicates that the obtained correlation index is reliable. The correlation prediction expression is(9)predFm=ζ¯+∑ζ∈neighKnζ,Kn−ζ¯×IDENu,v∑ζ∈neighKnIDENu,v,where predFm represents the prediction function for the recommended data; IDENu,v represents the association rule identification function; ζ represents the harmonic index; ζ¯ represents the harmonic average; u represents the surface correlation index; and v represents a progressive implicit correlation index. According to the above prediction expression, the association rules between different types of data are obtained. According to different values of association rules, the connection strength between big data in the Internet of Things can be determined.For the learning students of online courses, whether they are new students or old students who have studied many courses, when they choose a new course for learning, they often find that the contents of many courses are very similar. In this case, students usually check the evaluation of the course and choose the courses that has better quality and is more popular for learning. Each course in the cloud classroom website displays the total number of learners, students’ scores, the number of scoring, the number of comments, and all students’ comments. This information will affect students’ course selection. Therefore, it is also necessary to comprehensively evaluate the course quality in the recommendation. Among these factors, the number of comments on the course cannot represent the quality of the course because the content of the comments has positive and negative comments, so it is necessary to judge the course quality by viewing the comment content. In addition, there are many third-party institutions in the cloud classroom, and many courses have serious praise. For students, they can judge useful and useless evaluations according to their own identification, but it is very difficult for the algorithm in this paper to do this.The core idea of collaborative filtering is to analyze users’ interests according to user behavior records, find neighbor users who are similar to the target users (interests) in the user group, synthesize the evaluation of certain information by these neighbor users, form the system’s prediction of the target users’ resource preference degree, and then make corresponding recommendations according to these preference degrees. Because collaborative filtering has no special requirements for the recommended products, it can deal with objects which are difficult to describe in text, such as teaching image resources, so it is applied to the recommendation of sports network teaching resources. In this way, the data with association rules are arranged in a certain order. Fuzzy cluster analysis is used to obtain data features and match and divide objective and effective data. Taking the divided set shown in Figure2 as an example, different types of associated data are allocated to different sets according to data characteristics.Figure 2
Schematic diagram of the collaborative filtering process. (a) Original dataset. (b) Filtered data subset.In Figure2, learner-based collaborative filtering can find other learners with the same hobbies as online learners and recommend other students’ favorite information to online learners. Item-based collaborative filtering finds the most similar information according to the information used or scored by online learners. Because the project-based collaborative filtering method can establish a model in advance, it is more suitable for real-time online processing. However, the traditional collaborative filtering method requires learners to actively provide their preferences, which leads to some problems, such as additional student homework, changes in usage habits, and sparse data. One of the solutions is to use hidden indicators, of which the most important indicator is learners’ browsing time. Since the browsing time of the web page is a continuous value, it must be discretized first. The most common way is to divide it into several different interval categories and then analyze them with the traditional collaborative filtering method. This clear cutting is easy to cause the problem of “sharp edge.”In order to avoid the influence caused by the mismatch between the edge proportion of data features and node features, it is necessary to normalize the obtained results and set the original edge feature of data in the physical education network course teaching system asϑ^. The normalization process is as follows:(10)ϑ˜ija=ϑ^ija∑k=1Nϑ^ika,ϑija=∑k=1Nϑ˜ikaϑ˜jka∑υ=1Nϑ˜υka,where the edge feature ϑij∈Ra,i=1,2,⋯,m,j=1,2,⋯,m, of each data in the physical education network course teaching system represents the edge feature vector from the ϑij∈Ra,i=1,2,⋯,m,j=1,2,⋯,m derives and m is the number of data features [10, 11]. Since the above formula is a symmetric positive definite matrix, the data elements with maximum eigenvalues of i and ϑ^ are nonnegative numbers, and the following formula shall be satisfied:(11)ϑija≥0,∑i=1Mϑija=∑j=1Mϑija=1.The edge label of each data in the system can be defined according to the node label. The process is as follows:(12)dij=1,ifdi=dj,0,otherwise.When dealing with the data structure in the physical education network course teaching system, the number of iterative layers between the data edge and the data node is set toυis−1 and ϑijs−1 of S and S−1 layers. In the process of updating the node characteristics of the physical education network course teaching system, first, obtain other data node characteristics by calculating the edge characteristics and data aggregation ratio, and then use the above calculation process to update the data node characteristics of the first layer. The process is as follows:(13)υis=fυs∑jϑ˜ij1s−1υjs−1∑jϑ˜ij2s−1υjs−1;θJS,where fυs is the node update operation performed by the system, and the corresponding adjacent node information is ϑijs−1.The edge features of the physical education network course teaching system are updated by using the updated node features to obtain the similarity between the data nodes of each team, so as to integrate the data edge features. The update results are as follows:(14)ϑ¯ij1s=fesυis,υis;θϑsϑij1s−1∑kfϑsυis,υks;θϑsϑik1s−1/∑kϑik1s−1,ϑ¯ij1s=1−fesυis,υis;θϑsϑij2s−1∑k1−fϑsυis,υks;θϑsϑik2s−1/∑kϑik2s−1,ϑijs=ϑ¯ijs/1,where the similarity measurement network between nodes is fϑs and the parameter set is θϑs. The obtained results are mapped to the model of feature space as the initial node characteristics of the physical education network course teaching system, and the corresponding relationship between data information and reference nodes is obtained according to the updated edge characteristics.An embedded modelQφ is added to the training task of the physical education network course teaching system. If the data in the physical education network course teaching system are high-dimensional data, the model can be directly used to extract the node features, so as to reduce the reference quantity of the model and reduce the classification error. The process is as follows:(15)minφEwSmetaPtest;θ,φ,whereθ=ACtrain;φ,EkSmetaPtest;θ,φ,whereθ=ACtrain;φ.The metatraining task isw=Cjtrain,Pjtestj=1s, and the test task is K=Cjtrain,Pjtestj=1s. After S layers of network iteration, the data node characteristics and data edge characteristics of the final physical education network course teaching system are obtained. Then, the edge feature y^ij=ϑij1s is used to predict the final data edge label in the physical education network course teaching system. y^ij∈0,1 can be regarded as the same kind of probability of two nodes.


## 2.1. Design Principles of Recommending Methods for Network Curriculum Resources

(1)
Principle of Individualization. According to the characteristics of students’ physical and mental development and the needs of individualized learning, the individualized characteristics of web-based teaching should be reflected in the course design style.(2)
Humanization Principle. Interface design should be highly humanized, provide students with a simple and practical navigation system function to facilitate students to browse and find the required learning content, facilitate teachers to conduct online physical guidance, knowledge review, and essay corrections for students, facilitate online communication, evaluation guidance and feedback between teachers and students, and meet the teaching needs of teachers and students to the greatest extent.(3)
Principle of Richness and Openness. In order to improve the teaching quality of online courses, it is necessary to design and develop a practical tool for online course teaching, so as to ensure that teachers can smoothly transfer rich teaching resources of physical fitness knowledge and skills to the online course platform, make public display on the online course platform, and set up real-time communication areas and comment areas to facilitate teachers and students to learn, communicate, and discuss in real time or analyze, count, sort out, and summarize the teaching.(4)
Principle of Practicability. The overall design of the PE network course should be student-centered and conform to the students’ cognitive law, interest in learning, and psychological characteristics in teaching content, method selection, and textbook preparation. For example, in addition to presenting a certain special fitness knowledge and skills in the form of text, image, sound, animation, video, and other multimedia forms, it is necessary to properly supplement such knowledge as targeted and practical healthcare, diet and healthcare, fitness and health, and prevention and treatment of sports injuries, so as to meet the psychological needs of students, broaden their vision, enrich their knowledge structure, and improve their comprehensive quality.


## 2.2. Collection and Refinement of Network Curriculum Resources for Physical Education

Since the appearance of large-scale open online courses, the development of online education is in full swing. More and more students begin to get used to this new and rich learning form. As the number of online course resources increases and the variety of courses becomes more and more diverse, students often encounter difficult choices when considering the courses they want to study. Because of the characteristics of online courses, such as less text information, less student behavior information, and lack of evaluation information, the traditional recommendation algorithm cannot be directly applied to the recommendation of online courses, so there is still a lot of room for related work. Usually, the function of recommendation algorithm is grading and forecasting, but in practice, students only pay attention to the first few results of recommendation, so the recommendation system is more to solve the ranking problem. The single recommendation algorithm condition is too limited to provide the best recommendation result for students. To sum up, it is necessary to collect and refine the network curriculum resources of physical education.Firstly, the text information of the course is abstracted into the topic vector, which includes the name and introduction of the course. Then, the subject feature vector is constructed according to the students’ information, but after studying the students’ information in the dataset, it is found that few students fill in the personal interest label. Therefore, it is difficult to use this feature as a source of data to obtain students’ preferences.Firstly, the keyword in a period of time is extracted and integrated to obtain a dictionaryW. If the vocabulary contained in it is n and a student u is known, the following expression is used to represent the student’s file vector [5]:(1)profileu=vuw1,vuw2,...,vuwn,wi∈W, and the weight of the 33rd keyword in the file vector of i and u students is vuwi, which is used to describe the preference of u students for wi.In the process of keyword processing, the evaluation indextfidf of vocabulary importance is word frequency-antidocument frequency. The more a word appears in the file, the stronger the importance is. The word frequency-antidocument frequency index is defined through the following methods:(2)Rtft,d=ft,dmaxfw,d:w∈d,Rfidt,D=lnDd∈D:t∈d,Rtfidft,d,D=tft,d×idft,D.The word is expressed ast, the document is d, the antidocument frequency of word t is Ridft,D, and the number of occurrences of word t in document d is the initial frequency ft,D.Similarly, word frequency antistudent frequency (i.e.,tfiuf) is used to evaluate students’ preference for keywords, and its definition formula is as follows:(3)Rftt,u=ft,dumaxfw,du:w∈du,(4)Ridft,U=lnUu∈U:t∈du,(5)Rtfiuft,u,U=Rtft,u×Ridft,U,where the social network content published by students is du, the student collection is U, the occurrence frequency of vocabulary in students’ social network content is Rtft,u, the antistudent frequency of vocabulary is Ridft,U, and the occurrence frequency of vocabulary in du is ft,du.The lexical weight of the student profile vector is solved, and the student preference similarity is evaluated through the cosine similarity between the vectors [6]. The following formula shows the membership function of vectors V1 and V2:(6)cosineV1,V2=V1⋅V2V1⋅V2.Thus, the expression of preference similarity between the following studentu and student a is derived:(7)simcu,a=cosineprofileu,profilea=∑i=1nvuwi⋅vawi∑i=1nvu2wi⋅∑i=1nva2wi.According to the preference similarity, the learning preferences of teaching units and online learners are obtained, so as to determine which teaching units can be recommended to learners.


## 2.3. Recommendation of Teaching Units of Physical Education Network Courses

Teaching unit recommendation is an online process. When learners interact with the system, they can collect its access history information and search for the attributes of teaching resources. When recommending new teaching resources to learners, check the teaching units visited by learners, confirm whether the new teaching units are in the vicinity of the most similar teaching units, predict the recommended resources, and recommend the teaching units with the largest similarity value. According to the above obtained recommendation dataFm, the correlation between prediction F1,F2,…,Fm and prediction rules are shown in Figure 1.Figure 1
Schematic diagram of prediction rules in neighbors of most similar teaching units.Based on Figure1, different prediction methods are set according to multiple prediction conditions, and the surface correlation and implicit correlation are grasped to obtain the detailed correlation degree value of differentiation [7]. Create a set R and make the set R=F1,F2,…,Fm. If the setting k1,k2,…,km is a prediction condition corresponding to the recommended data F1,F2,…,Fm, the prediction coefficient calculation expression is(8)Kn=ωm×gmFm∩gm−1Fm−1∑m=1nkm−ks,where Kn represents the set n prediction coefficients; ωm represents prediction constraints; gmFm represents m predictions of recommended data Fm; gm−1Fm−1 represents m−1 predictions for the prediction data Fm; and ks represents the adjustment index under the influence of prediction intensity s at each prediction [8, 9]. Predict the correlation degree of the recommended data according to the calculated prediction coefficient. When the result is positive, it indicates that the obtained correlation index is reliable. The correlation prediction expression is(9)predFm=ζ¯+∑ζ∈neighKnζ,Kn−ζ¯×IDENu,v∑ζ∈neighKnIDENu,v,where predFm represents the prediction function for the recommended data; IDENu,v represents the association rule identification function; ζ represents the harmonic index; ζ¯ represents the harmonic average; u represents the surface correlation index; and v represents a progressive implicit correlation index. According to the above prediction expression, the association rules between different types of data are obtained. According to different values of association rules, the connection strength between big data in the Internet of Things can be determined.For the learning students of online courses, whether they are new students or old students who have studied many courses, when they choose a new course for learning, they often find that the contents of many courses are very similar. In this case, students usually check the evaluation of the course and choose the courses that has better quality and is more popular for learning. Each course in the cloud classroom website displays the total number of learners, students’ scores, the number of scoring, the number of comments, and all students’ comments. This information will affect students’ course selection. Therefore, it is also necessary to comprehensively evaluate the course quality in the recommendation. Among these factors, the number of comments on the course cannot represent the quality of the course because the content of the comments has positive and negative comments, so it is necessary to judge the course quality by viewing the comment content. In addition, there are many third-party institutions in the cloud classroom, and many courses have serious praise. For students, they can judge useful and useless evaluations according to their own identification, but it is very difficult for the algorithm in this paper to do this.The core idea of collaborative filtering is to analyze users’ interests according to user behavior records, find neighbor users who are similar to the target users (interests) in the user group, synthesize the evaluation of certain information by these neighbor users, form the system’s prediction of the target users’ resource preference degree, and then make corresponding recommendations according to these preference degrees. Because collaborative filtering has no special requirements for the recommended products, it can deal with objects which are difficult to describe in text, such as teaching image resources, so it is applied to the recommendation of sports network teaching resources. In this way, the data with association rules are arranged in a certain order. Fuzzy cluster analysis is used to obtain data features and match and divide objective and effective data. Taking the divided set shown in Figure2 as an example, different types of associated data are allocated to different sets according to data characteristics.Figure 2
Schematic diagram of the collaborative filtering process. (a) Original dataset. (b) Filtered data subset.In Figure2, learner-based collaborative filtering can find other learners with the same hobbies as online learners and recommend other students’ favorite information to online learners. Item-based collaborative filtering finds the most similar information according to the information used or scored by online learners. Because the project-based collaborative filtering method can establish a model in advance, it is more suitable for real-time online processing. However, the traditional collaborative filtering method requires learners to actively provide their preferences, which leads to some problems, such as additional student homework, changes in usage habits, and sparse data. One of the solutions is to use hidden indicators, of which the most important indicator is learners’ browsing time. Since the browsing time of the web page is a continuous value, it must be discretized first. The most common way is to divide it into several different interval categories and then analyze them with the traditional collaborative filtering method. This clear cutting is easy to cause the problem of “sharp edge.”In order to avoid the influence caused by the mismatch between the edge proportion of data features and node features, it is necessary to normalize the obtained results and set the original edge feature of data in the physical education network course teaching system asϑ^. The normalization process is as follows:(10)ϑ˜ija=ϑ^ija∑k=1Nϑ^ika,ϑija=∑k=1Nϑ˜ikaϑ˜jka∑υ=1Nϑ˜υka,where the edge feature ϑij∈Ra,i=1,2,⋯,m,j=1,2,⋯,m, of each data in the physical education network course teaching system represents the edge feature vector from the ϑij∈Ra,i=1,2,⋯,m,j=1,2,⋯,m derives and m is the number of data features [10, 11]. Since the above formula is a symmetric positive definite matrix, the data elements with maximum eigenvalues of i and ϑ^ are nonnegative numbers, and the following formula shall be satisfied:(11)ϑija≥0,∑i=1Mϑija=∑j=1Mϑija=1.The edge label of each data in the system can be defined according to the node label. The process is as follows:(12)dij=1,ifdi=dj,0,otherwise.When dealing with the data structure in the physical education network course teaching system, the number of iterative layers between the data edge and the data node is set toυis−1 and ϑijs−1 of S and S−1 layers. In the process of updating the node characteristics of the physical education network course teaching system, first, obtain other data node characteristics by calculating the edge characteristics and data aggregation ratio, and then use the above calculation process to update the data node characteristics of the first layer. The process is as follows:(13)υis=fυs∑jϑ˜ij1s−1υjs−1∑jϑ˜ij2s−1υjs−1;θJS,where fυs is the node update operation performed by the system, and the corresponding adjacent node information is ϑijs−1.The edge features of the physical education network course teaching system are updated by using the updated node features to obtain the similarity between the data nodes of each team, so as to integrate the data edge features. The update results are as follows:(14)ϑ¯ij1s=fesυis,υis;θϑsϑij1s−1∑kfϑsυis,υks;θϑsϑik1s−1/∑kϑik1s−1,ϑ¯ij1s=1−fesυis,υis;θϑsϑij2s−1∑k1−fϑsυis,υks;θϑsϑik2s−1/∑kϑik2s−1,ϑijs=ϑ¯ijs/1,where the similarity measurement network between nodes is fϑs and the parameter set is θϑs. The obtained results are mapped to the model of feature space as the initial node characteristics of the physical education network course teaching system, and the corresponding relationship between data information and reference nodes is obtained according to the updated edge characteristics.An embedded modelQφ is added to the training task of the physical education network course teaching system. If the data in the physical education network course teaching system are high-dimensional data, the model can be directly used to extract the node features, so as to reduce the reference quantity of the model and reduce the classification error. The process is as follows:(15)minφEwSmetaPtest;θ,φ,whereθ=ACtrain;φ,EkSmetaPtest;θ,φ,whereθ=ACtrain;φ.The metatraining task isw=Cjtrain,Pjtestj=1s, and the test task is K=Cjtrain,Pjtestj=1s. After S layers of network iteration, the data node characteristics and data edge characteristics of the final physical education network course teaching system are obtained. Then, the edge feature y^ij=ϑij1s is used to predict the final data edge label in the physical education network course teaching system. y^ij∈0,1 can be regarded as the same kind of probability of two nodes.


## 3. Recommendation of Physical Education Network Curriculum Resources Based on Collaborative Filtering Technology

### 3.1. Generate the Similarity Matrix

The collaborative filtering recommendation algorithm is used to mine the students’ browsing characteristics of online sports courses, such as browsing time and evaluation quality, in the teaching system log of online sports courses, and divide this series of operations into weights to obtain students’ evaluation on the knowledge item and make a student knowledge scoring matrix [12, 13].(1)
The student interest model is constructed based on the training set, and the prediction is carried out on the test set. The data of the training set are the knowledge dataset of the top 75% of the student knowledge score matrix. The test set data are the remaining 25% knowledge data item set [14, 15].(2)
The similarity between knowledge items is calculated by the cosine similarity method. Then, the similarity calculation method between knowledge itemi and j is shown in formula (7):(16)sini,j=cosi,j=ijij.(3)
Generate knowledge cosine similarity matrixTSK.


### 3.2. Generate the Incidence Matrix

Calculate the correlation degree between knowledge items, and generate the correlation matrixTRK. The calculation process is as follows:(1)
The input of the algorithm is the knowledge item in the knowledge tree(2)
In order to clarify the support of each knowledge item, the frequent 1-item set is obtained by single scanning the dataset(3)
The dataset is scanned by the algorithm, and the subset function is used to calculate the total support of the candidate 2-item set(4)
In order to effectively prevent the generation of low-confidence and high-similarity results, the confidence of all candidate sets with support less than minsup is set to 0(5)
The confidence conf of the candidate 2-item set is calculated to generate the knowledge incidence matrixTRK


### 3.3. Obtain Recommendation Results

In order to obtain matrixTSRK of students’ interest in knowledge items, add the knowledge item similarity matrix TSK and the knowledge item association matrix TRK, and TSRK = TSK+TSRK.(1)
Based onTSRK, seek the first knowledge item related to the target Kknowledge item.(2)
The calculation method of students’ interest in knowledge items is shown in the following formula:(17)puj=∑i∈NuASRKj,kFjiRui,whereFji and Rui, respectively, represent the corresponding values of knowledge items j and i in TSRK and the students’ interest in knowledge items; Nu and TSRKj,k, respectively, represent the set of knowledge items that students like and the K knowledge item sets with the highest sum of acquaintance and correlation with knowledge item j. The higher the degree of relevance to the knowledge item that students are interested in, the higher its ranking in the student recommendation list, so as to complete the design of the physical education online course resource recommendation method based on collaborative filtering technology.


## 3.1. Generate the Similarity Matrix

The collaborative filtering recommendation algorithm is used to mine the students’ browsing characteristics of online sports courses, such as browsing time and evaluation quality, in the teaching system log of online sports courses, and divide this series of operations into weights to obtain students’ evaluation on the knowledge item and make a student knowledge scoring matrix [12, 13].(1)
The student interest model is constructed based on the training set, and the prediction is carried out on the test set. The data of the training set are the knowledge dataset of the top 75% of the student knowledge score matrix. The test set data are the remaining 25% knowledge data item set [14, 15].(2)
The similarity between knowledge items is calculated by the cosine similarity method. Then, the similarity calculation method between knowledge itemi and j is shown in formula (7):(16)sini,j=cosi,j=ijij.(3)
Generate knowledge cosine similarity matrixTSK.


## 3.2. Generate the Incidence Matrix

Calculate the correlation degree between knowledge items, and generate the correlation matrixTRK. The calculation process is as follows:(1)
The input of the algorithm is the knowledge item in the knowledge tree(2)
In order to clarify the support of each knowledge item, the frequent 1-item set is obtained by single scanning the dataset(3)
The dataset is scanned by the algorithm, and the subset function is used to calculate the total support of the candidate 2-item set(4)
In order to effectively prevent the generation of low-confidence and high-similarity results, the confidence of all candidate sets with support less than minsup is set to 0(5)
The confidence conf of the candidate 2-item set is calculated to generate the knowledge incidence matrixTRK


## 3.3. Obtain Recommendation Results

In order to obtain matrixTSRK of students’ interest in knowledge items, add the knowledge item similarity matrix TSK and the knowledge item association matrix TRK, and TSRK = TSK+TSRK.(1)
Based onTSRK, seek the first knowledge item related to the target Kknowledge item.(2)
The calculation method of students’ interest in knowledge items is shown in the following formula:(17)puj=∑i∈NuASRKj,kFjiRui,whereFji and Rui, respectively, represent the corresponding values of knowledge items j and i in TSRK and the students’ interest in knowledge items; Nu and TSRKj,k, respectively, represent the set of knowledge items that students like and the K knowledge item sets with the highest sum of acquaintance and correlation with knowledge item j. The higher the degree of relevance to the knowledge item that students are interested in, the higher its ranking in the student recommendation list, so as to complete the design of the physical education online course resource recommendation method based on collaborative filtering technology.


## 4. Example Test

Data source is a government network learning website, and the network learning system includes teaching materials, students, question bank and scores, and other databases. Its modules include course readings, grade calculations, and tests. In this study, we selectedw nonzero items and removed the rest. The first N teaching units recommended by the recommendation system and the deleted items are compared to evaluate the recommendation effect.Set minimum browsing time todmin: 3 seconds. Maximum browsing time is dmax: 1200 seconds. Value of parameter da: the hundredth of a teaching unit’s browsing time. Parameter dc value: teaching unit browsing time, the first percent c. Number of window sizes: 2. Most similar number of teaching unit neighbors: all teaching units. Recommended number of teaching units N: 2, 3, 5, 7, 9, and 11. Rule of thumb dmin = 3 seconds and dmax = 1200 seconds. dmin and dmax can reduce unreliable teaching unit references. The browsing time of the removed unit was less than dmin, and the restricted unit browsing time was less than dmax. There are two ways to compute da and dc, browsing time for each teaching unit or browsing time for all teaching units.The test process and its constraints are as follows: first, the browsing time of the sequential teaching unit and the browsing time of the calculation teaching unit. Second, the mobile window remains the largest for the historical depth of the online learner, that is, the number of units that the online learner has seen before being recommended is not fixed, and the number of recommended units is not fixed.The average absolute error (MAE) and root-mean-square error (RMSE) are introduced to measure the overall recommendation performance of the proposed method. The smaller the values of both, the better the recommendation performance. At different test set ratios, the MAE and RMSE results before and after use are shown in Figures3 and 4, respectively. Zhang’s [2] method refers to the teaching information resource management method based on user interests, and Huang et al.’s [3] method refers to the intelligent recommendation method of online courses.Figure 3
MAE comparison results.Figure 4
RMSE comparison results.By analyzing Figures3 and 4, it can be seen that the MAE and RMSE results after the use of the method in this paper are always lower than the two index results before the use of the method in this paper. When the proportion of the test set is less than 30%, the MAE results show a downward trend. When the proportion of the test set is more than 30%, the MAE results increase rapidly, and the maximum MAE value is about 0.90. The MAE results before the use of this method showed the characteristics of first increasing, then decreasing, and then increasing.In the process of online course learning, much content information will be involved, including course-related information, such as text description, course difficulty, student activity, and course lecturer, and student-related information, such as course learning time, learning progress, course collection, and student social relations. This paper will use this information to quantify this information into different features. After extracting each feature, we will use the ranking learning framework to integrate it and finally get the recommendation results of online courses. In order to avoid the randomness of the experimental test, 50 different types of students are selected to analyze the personalized recommended contents of each course system. Finally, the recommended contents in each course system are matched with the keyword conditions corresponding to the prestudent behavior. The matching degree is calculated by a professional data matching instrument. The comparison test conclusion is shown in Figure5.Figure 5
Recommended process stability test results.As can be seen from Figure5, for 1000 keywords, the single average matching degree of this method is the highest, indicating that its recommendation process is stable. This is because the collaborative filtering algorithm finds the most similar neighbors through similarity calculation according to the comparison between student behavior and other student behaviors (scores, comments, etc.), and according to the interests or preferences of the most similar neighbors, predict the student’s interests or preferences.Taking the learning curve of physical education network curriculum resources as the fitting ability index of this method, calculate theH-mean value of physical education network curriculum resources’ test sample set and physical education network curriculum resources’ training sample set, that is, F1 value. The larger the value, the higher the early warning accuracy of the model, and test the fitting ability of this method. The results are shown in Figure 6.Figure 6
Fitting ability test results.Analysis of Figure6 shows that the F1 numerical curve of the training sample set and the testing sample set is smooth with the increase of the ratio of the dataset, the fluctuation is not big, the F1 numerical curve of the testing sample set is lower than that of the training sample set, but the value difference is not big; the F1 value of the two sets is more than 0.9, the value shows that the model is not affected by the ratio of the dataset and does not show the phenomenon of overfitting and insufficient fitting, the model fitting ability is strong, and robustness is good.


## 5. Conclusion

The network course support platform supports independent, leading, discussion, and other types of network courses. Its curriculum management is flexible, and learning activities are diverse, with classes and group functions. Ability to track and analyze learning records has test functions, supports scores and hierarchical evaluation, and supports international standard curriculum learning. In a word, the recommendation method based on collaborative filtering technology provides an ideal environment for education and realizes the diversification and individuation of learning resources. The experimental results show that the average absolute error and mean square error of the proposed method are 0.59% and 1.12, respectively, which has a high matching degree, and theF1 value reaches 0.9, which can better realize the recommendation of network resources.With the development of the internet, especially the popularization of the mobile internet, online courses will be accepted by more and more students, and the number of online courses and students will increase greatly. How to provide personalized recommendation services for each student is an important problem for related research under the condition of guaranteeing the system performance. In this paper, the problem of online course recommendation is studied, but there are still many deficiencies in the research because of the insufficient size of the dataset and insufficient consideration of the conditions.(1)
Besides the brief introduction and teaching catalogue, there are a large number of students’ comments on the text information related to the course, the content of which can well reflect the quality and difficulty of the course. In the future, we may consider digging in this regard and consider the course from a richer perspective.(2)
Students’ learning data contain a lot of contents reflecting the learning situation, such as the progress of learning and the time of learning, and this information reflects the students’ interest in the course to some extent. For example, some courses have only been taken for one class and then they are not continued. After the students have finished studying, they find that they are not interested in the content of the course. The information can be used to enhance the evaluation of students’ curriculum preferences in the future.



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*Source: 1082697-2021-10-29.xml*