Collaborative Filtering Recommendation Algorithm Research Articles

The Path of Digital Construction of Civics and Politics in Professional Course Curriculum of Higher Vocational Colleges and Universities Based on the Concept of OBE

Abstract This paper explores the principle of deep reinforcement learning algorithm application and constructs the digital teaching model of Civics through the recurrent neural network, Markov decision process, Actor-Critic algorithm and collaborative filtering recommendation algorithm. On this basis, the state representation model and decision-making model are invoked to improve the diversity optimization recommendation of the deep reinforcement learning algorithm. Aiming at the problems existing in the practical application of the OBE concept, the model constructed in this paper is utilized to propose a multi-platform integration of the Civics digital learning model. The objective of empirical and simulation experiments is to confirm the model’s implementation and recommendation effects, respectively. In the validation of hyperparameter d and N values, the DQN model achieves the optimal recommendation effect when the d values are 65 and 63 on the HetRec and MovieTweetings datasets. During the implementation of the digital teaching evaluation of the course Civics, more than 60% of the students rated the teacher’s feedback session higher. Overall, the 6 dimensions were rated higher, and the number of students who chose to be fully compliant and conforming was more than 200.

The Path of Civic Education and Intelligent Strategies for Counselors in the Background of Artificial Intelligence

Abstract In this paper, starting from Civic Education under the conditions of artificial intelligence application, an intelligent platform for counselors is constructed as a path for Civic Education, in which Spark’s big data platform is used to collect and organize the data. A collaborative filtering recommendation algorithm with a hybrid self-coding network model is proposed, and a recommendation service framework is built based on this algorithm and recommendation algorithm evaluation index to realize personalized recommendations of educational resources. The empirical analysis was conducted for the platform constructed in this paper, and the results showed that the intelligent platform for counselors and the learning analytics platform were used by most teachers of college Civics courses, accounting for 55.64% and 43.25%, respectively, and compared with the traditional teaching mode, the intelligent platform teaching was more popular among teachers and students.

Numerical analysis-oriented Kruskal algorithm for the analysis and integration of effective components of university music pedagogy

Abstract This paper constructs a collection of effective teaching methods based on the improved K-Means clustering algorithm for clustering and dividing effective components of music teaching in colleges and universities. By analyzing the personalized content recommendation system, we can construct a recommendation system based on teaching content using information retrieval and filtering techniques. The collaborative filtering recommendation algorithm is used to ensure the accurate placement of teaching content. The Kruskal algorithm is used to find the minimum spanning tree of teaching effective components, and the K-means clustering principle is applied to the division of music teaching effective components, and the cluster of effective teaching components is divided by the clustering algorithm. According to the findings, mind-body integration and the teaching goal of valuing creativity were classified as effective teaching components in music. Personal aesthetics had a 0.6 influence on musical creativity, and a free environment had a 0.3 influence.

Exploring the Innovation Practice of Teaching Civics Education in Universities in the Network Era

Abstract One of the key difficulties confronting universities today is how to use the current technology to raise educational standards and support educational reform. This is due to the ongoing growth of network technology. This study primarily explores the collaborative filtering recommendation algorithm-based recommendation model for college civics teaching materials. Analyzed are both the functional and non-functional demands for collegiate civic and political science teaching materials. For these requirements, the collaborative filtering recommendation algorithm is put out, and it is enhanced so that it may be applied in real-world settings. In order to execute the model, the system architecture and process are established, and the impacts of teaching interventions are used to examine the outcomes of the suggested practical teaching of civic and political science teaching materials in colleges and universities. The findings revealed that the p-values of the significance probabilities were, respectively, 0.056, 0.007, and 0.235 for the entrance exam and the first semester final examination, the entrance examination and the second semester final examination, and the first semester final examination and the second semester final examination. The collaborative filtering recommendation algorithm-based recommendation model for teaching resources recommendation of university thinking and politics proposed in this paper is effective in enhancing students’ learning autonomy and promoting the exchange of ideas. It has a certain effect.

Research on the integration path of cultural creative industry and tourism industry based on collaborative filtering recommendation algorithm

Abstract In order to increase regional economic benefits and attract more tourists for local economic development. This paper adopts different ways to evolve industrial integration, enrich and expand the theoretical system of the industrial economy, establish new tourism strategies to meet personalized and diversified needs and make the tourism sector achieve industrial integration. Useful data are selected to filter cultural and creative, and tourism industry information by mining hidden information of coding sequences and finding support data items. Using collaborative filtering recommendation method to obtain the preference degree of fused data items, populate the data according to behavioral speculation, calculate the utility matrix similarity, and clarify the development of industry fusion path in the context of big data. The results show that the average error rate after fusion does not exceed 4%, and the fusion path of cultural and creative industries and the tourism industry improve the regional economy and the overall local living conditions.

Weight prediction and recognition of latent subject terms based on the fusion of explicit & implicit information about keyword

Weight prediction and recognition of subject term in documents is widely used in application fields such as literature recommendation and keyword retrieval. Unlike traditional methods which only focus on searching for subject terms from existing keywords in documents, this paper proposes a recognition method of latent subject term outside documents based on a one-class collaborative filtering algorithm that fuses explicit and implicit information. A matrix factorization model based on the analysis of document activity and subject term popularity is constructed to measure the correlation probability between documents and subject terms that do not appear in the current document. After these subject terms being divided into Latent Subject Terms (LST) and Irrelevant Subject Terms (IST), two methods for predicting the weight of these types of subject terms is introduced, which include Hybrid Filling with Preference Coefficients (HFPC), and Zero Filling. In order to verify the effectiveness of subject term recognition, many collaborative filtering recommendation algorithms are conducted with the filled document keyword matrix. On the basis of not changing these algorithms, MAE and FCP can be improved by 28.01% and 22.79% at most, while P@N and NDCG@N can be improved by 22.37% and 27.06%, respectively.

Research on the innovation of ideological and political education for college students based on the Civic and Political Science Cloud Education Platform

Abstract Innovative ideological and political education for college students is to help better students improve their ideological quality and political literacy. In this paper, under the background of big data, the Civic and Political Cloud Education Platform is constructed using big data technology, which is mainly composed of four main parts: model layer, view layer, control layer, and basic services. Student behavior data is collected and analyzed through a collaborative filtering recommendation algorithm under big data collection and analysis technology, and performance simulation analysis is conducted for the Civic and Political Science Cloud Education Platform. From the performance analysis, when the number of concurrent users of the Civic Education Cloud Platform is 80-120, the average transaction processing per second is stable at 10.5 s. From the practicality, the Civic Education Cloud Platform built by big data can effectively explore the value of Civic Education teaching resources. This shows that the Civic and Political Science cloud education platform is an effective way to innovate and develop the ideological and political education of college students, and through this platform, the ideological quality and political literacy of college students can be effectively improved.

Retracted: Intelligent Sports Auxiliary Training Method Based on Collaborative Filtering Recommendation Algorithm

Retracted: Intelligent Sports Auxiliary Training Method Based on Collaborative Filtering Recommendation Algorithm

Analysis of media content recommendation in the new media era considering scene clustering algorithm

Abstract With the advent of the new media era, we face the problem of information overload every day, based on which this paper proposes a scenario-conscious clustering algorithm for recommending media content. The improved K-means algorithm is used to cluster the media content, an initial cluster center is randomly selected, and the remaining initial cluster centers are obtained by executing the improved algorithm to reduce the number of iterations and avoid neighboring situations. The clustering algorithm is then compared with content-based recommendation techniques, KNN recommendation algorithm, and collaborative filtering recommendation algorithm in terms of accuracy, recall, MAE value, and execution time. The clustering algorithm is significantly better than the other algorithms regarding accuracy and recall. The accuracy of the clustering algorithm is 0.4 when the recommendation sequence is 30, which is 0.4 higher than the collaborative filtering technique, 0.1 higher than the KNN, and 0.12 higher than the content recommendation method. In terms of MAE value, the clustering algorithm outperforms the other algorithms when the number of nearest neighbors is selected to be above 20. In terms of execution time, the longer the amount of data, the more obvious the advantage of the clustering algorithm. Therefore, the applicability and reliability of the model proposed in this paper for media content recommendation are verified in terms of accuracy, recall and execution time, which meet the design requirements.

Research on personalized recommendation method of preschool e-learning resources

Abstract E-learning is a very popular learning method at this stage, and Internet learners need to invest much time to retrieve the required e-learning network resources. In building the existing e-learning resources, the cognitive level, thinking ability, learning style, and other factors of learners are not considered. For this challenge, the paper gives the recommendation methods of the collaborative filtering algorithm model and learner model, compares the advantages and disadvantages of these models and traditional recommendation methods using the data of mean square error, response time, and accuracy, and examines the students’ suggestions for this recommendation method in the field. The collaborative filtering recommendation technique had the highest completion rate, above 0.7 for 5-20 recommendations and above 0.75 and 0.8 for 20-35. The rate of the learner model is stable between 0.7 and 0.8. In contrast, the detection rate of traditional methods is between 0.6 and 0.7. The collaborative filtering recommendation method outperforms the other two methods regarding completeness, response time, accuracy, and F1 value. In terms of satisfaction, the collaborative filtering recommendation algorithm had the highest satisfaction rate, followed by the learner model; the collaborative filtering method had the best results in improving learning performance. Therefore, personalized recommendation methods can improve learning efficiency and reduce retrieval time, and the collaborative filtering recommendation method is the most effective among the two methods proposed in this paper.

Film and television art innovation in network environment by using collaborative filtering recommendation algorithm

Film and television art innovation in network environment by using collaborative filtering recommendation algorithm

Analyzing the misleading information on Covid-19 using MBCFWS4

The COVID-19 pandemic had a major effect on almost every area of human lives in the majority of the world's countries. Misinformation spreads quickly in the initial phases of the COVID-19 pandemic in different forms, such as fear, distortion, contraindication assumption, and others. False and misleading advertisements harm millions of individuals. In recent research, there are more advanced techniques have been used to address the misinformation about the COVID-19 pandemic. But they are self-reported and probabilistic data during the lockdown period. So, it was difficult to find the respondents' perceptions based on sharing the COVID-19 misinformation. This proposed work analyzed and filtered some optimized factors to analyze the misinformation such as fake reports, fake remedies, conspiracy, susceptibility, vaccine rumors, social media, vitamin D prevents corona, political corona, socio-Economic, and more side effects after getting vaccinated. Collaborative filtering (CF) is the most efficient recommender system, and it is extensively utilized by a broad range of research institutions and enterprises, as well as being used in practice. It consists of two types of CF namely Memory-based CF and Model-based CF. In this work, Memory-based CF recommendation algorithm is combined with a similarity measure called MBCFWS4 to analyze the similarity measure between the factors to conclude the most impact factor. The Primary and secondary dataset helps to identify the respondents' perception based on the COVID-19 misinformation. The efficiency comparison of the proposed work is measured in terms of Precision, Recall & F-measure and found that this analysis using MBCFWS4 is outperforming well than the others as MBCFWS4 predicted accurately and revealed the conclusion based on the COVID-19 misinformation.

Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks

With the rapid growth of the Internet, a wealth of movie resources are readily available on the major search engines. Still, it is unlikely that users will be able to find precisely the movies they are more interested in any time soon. Traditional recommendation algorithms, such as collaborative filtering recommendation algorithms only use the user’s rating information of the movie, without using the attribute information of the user and the movie, which has the problem of inaccurate recommendations. In order to achieve personalized accurate movie recommendations, a movie recommendation algorithm based on a multi-feature attention mechanism with deep neural networks and convolutional neural networks is proposed. In order to make the predicted movie ratings more accurate, user attribute information and movie attribute information are added, user network and movie network are presented to learn user features and movie features, respectively, and a feature attention mechanism is proposed so that different parts contribute differently to movie ratings. Text features are also extracted using convolutional neural networks, in which an attention mechanism is added to make the extracted text features more accurate, and finally, personalized movie accurate recommendations are achieved. The experimental results verify the effectiveness of the algorithm. The user attribute features and movie attribute features have a good effect on the rating, the feature attention mechanism makes the features distinguish the degree of importance to the rating, and the convolutional neural network adding the attention mechanism makes the extracted text features more effective and achieves high accuracy in MSE, MAE, MAPE, R2, and RMSE indexes.

A Personalized Recommendation Algorithm for Semantic Classification of New Book Recommendation Services for University Libraries

With the rapid development of information technology and the Internet, it is difficult for university readers to find books of real interest or value from a large number of books by relying only on traditional retrieval-based services. This paper applies data mining technology and personalized recommendation algorithm based on semantic classification for new book recommendation service in university libraries. The personalized recommendation algorithm based on semantic classification establishes a book feature model and a reader preference model based on title keywords. The different recommendation strategies in the system framework are detailed. For the borrowing data of different colleges and departments, the improved association rule algorithm is used to mine the book association rules, and the reader’s borrowing history is matched with the association rules to generate a book recommendation list; according to the reader’s borrowing preference characteristics, the reader preference model is used as the basis. Class subdivision and then combined with the book feature model and reader preference model, the collaborative filtering recommendation algorithm and the content-based recommendation algorithm are applied to generate a book recommendation list. The active service method not only improves the service level of the university library, makes the development of the university library more comprehensive and humanized but also explores the potential information needs of readers, improves the borrowing rate of books in the collection, and maximizes the utilization rate of book resources. In the experiment of this paper, the personalized recommendation algorithm division of semantic classification is adopted. According to the division of its algorithm, the corpus is divided into 9603 training documents and 3299 test documents, with certain accuracy.

Intelligent Sports Auxiliary Training Method Based on Collaborative Filtering Recommendation Algorithm

In recent years, people’s living standards are rising, and the demand for health is also rising. National physical exercise has become the current trend. Using personalized recommendation technology to screen appropriate information and assist people in sports training can improve the efficiency of users to obtain relevant information and improve users’ physical quality. Therefore, the collaborative filtering recommendation algorithm is optimized by combining bisection, and the intelligent recommendation model of sports training resources is constructed based on the algorithm. This paper uses the improved algorithm to calculate the similarity between users. Compared with other traditional algorithms, the user algorithm in this paper has higher accuracy and certain reference value. To sum up, the intelligent recommendation model of sports training resources proposed in the research can more accurately recommend suitable sports training resources for users, so as to assist users in correct sports training and improve users’ physical quality, which plays an improvement of the physical quality of the whole people in China.

The Adoption Factors of Mobile Games in the Wireless Environment.

The factors that influence how people play mobile games have been studied from a variety of perspectives in the wireless broadband environment. The original data in the background of the game, such as user operation records, consumption records, and social behavior records, are converted into user attributes, user tags are generated, and data sets are constructed in this study, which primarily uses data mining technology to study user behavior and form user portraits. By incorporating the similarity of players' subspace interests into the CFR (collaborative filtering recommendation) algorithm, a personalized game recommendation model, as well as the relationship management level of mobile game players, is created. The final fusion model's ROC-AUC value is 0.921, which has a percentile enhancement effect, according to the results. The findings show that using a personalized game recommendation model can help to improve the scalability of the CFR algorithm and the impact of data scarcity on the quality of mobile games recommended by players.

Analysis of the Influence of Hybrid Personalized Recommendation Algorithm on the Employment Tendency of Hotel Management Major

As the pillar industry of tourism, the hotel industry plays an important role in the present and future development of tourism. Therefore, the industry’s demand for hotel management professionals is increasing. In this paper, the mixed personalized recommendation algorithm is used to carry out descriptive statistical analysis on the results, and the characteristics of the employment tendency of college students majoring in hotel management are preliminarily understood. In addition, the influence of human capital and social capital on college students’ employment tendency is discussed. The results show that, through experiments, the hybrid personalized recommendation algorithm proposed in this paper is compared with two traditional collaborative filtering recommendation algorithms based on users, Pearson similarity and cosine similarity. The algorithm in this paper has smaller MAE, which shows that the algorithm proposed in this paper has higher recommendation quality. Through this research, it is helpful to have a more comprehensive understanding of the actual situation and characteristics of the employment tendency of hotel management major college students, and it also provides the policy basis for the government and relevant departments to make employment decisions.

A collaborative filtering recommendation algorithm based on fuzzy C-means clustering

With the rapid growth of data scale, the problems of collaborative filtering recommendation algorithm are more and more obvious, such as data sparsity, cold start, scalability, and the change of user interest over time. About the existing problems, we introduce the fuzzy clustering and propose a collaborative filtering algorithm based on fuzzy C-means clustering. The algorithm performs fuzzy clustering on the item attribute information to make items belonging to different categories in different membership degree, increases the data density, effectively reduces the data sparsity, and solves the issue that the inaccuracy of similarity leads to the low recommendation accuracy. Meanwhile, the algorithm introduces the time weight function. Different evaluation times give different time weight values, and recently evaluated items are more representative of the user current interest, so we give a higher weight value, and early evaluated items have less effect on the user current interest, thus the weight value are relatively lower. The experimental results show that our algorithm can effectively alleviate the data sparsity problem and time migration of users preferences, thus achieve better performance.

Digital Movie Recommendation Algorithm Based on Big Data Platform

How to associate films with users among various film data and help users get useful information is a big problem we face. The recommendation system aims to provide users with accurate project recommendations, which can effectively solve the problem of information explosion caused by a large amount of data. Traditional recommendation systems are widely used in movie shopping. Aiming at this problem, this paper designs and develops a collaborative filtering recommendation algorithm based on big data platform. Firstly, the depth is deeper than the traditional automatic coding network, and the new activation function is used to generate the depth feature vector. Secondly, the model can describe both linear and nonlinear features of movie data, which further improves the extraction ability of nonlinear features. Experimental results show that the proposed algorithm is effective and can bring better user experience and economic benefits to consumers.

Interaction Design of Educational App Based on Collaborative Filtering Recommendation

With the advent of the 5G digital era, cell phones are becoming ubiquitous in all aspects of our lives, and the increasing demand for remote interaction makes the app interaction experience an indispensable part of our lives. Due to the operational characteristics of gesture interaction in the interface of a smart terminal application (app), this mode of human-computer interaction has become the mainstream mode of human-computer interaction. Educational app is the result of a combination between mobile Internet technology and education, which not only provides a more efficient and convenient method of learning for each subject but also expands the possibilities for teaching each subject through intelligent interaction. On this basis, this paper proposes an educational app design method based on collaborative filtering recommendations and investigates ways to improve the use of mobile apps to create an interactive teaching mode. Simultaneously, this paper combines user activity, item popularity, and time factors to comprehensively measure user visibility of items and incorporates them into the collaborative filtering recommendation algorithm in order to effectively mitigate the effects of data sparsity and user selection bias and improve recommendation results.