Collaborative Recommendation Algorithm Research Articles

Blended Teaching Mode Model of College English Based on Collaborative Filtering Recommendation Algorithm

The incorporation of collaborative filtering recommendation algorithms into blended teaching approaches for college English marks a big step forward in language education. This study investigates the theoretical underpinnings, practical ramifications, and pedagogical issues for adding collaborative filtering algorithms into blended learning environments. Collaborative filtering algorithms produce individualized learning resource recommendations based on user interactions and preferences, increasing student engagement and motivation. However, issues like as data privacy, algorithmic biases, and user approval must be addressed to ensure that collaborative filtering is implemented effectively in the classroom. This study explains the potential of collaborative filtering recommendation algorithms to improve language learning results in college English courses by doing a thorough literature review. It also provides ideas for educators and policymakers on how to handle the complexity of incorporating collaborative filtering recommendation algorithms into blended learning models. Finally, this paper adds to the promotion of novel pedagogical approaches and the improvement of language learning experiences among college students. As a result, the enhanced collaborative filtering recommendation system had a slightly greater recall rate and precision than the pre-optimized method.

Hybrid Teaching Model of College English Based on Collaborative Filtering Recommendation Algorithm

A recommendation system is a powerful tool that analyzes user preferences and behaviors to provide personalized suggestions or recommendations. By leveraging algorithms and data analysis techniques, recommendation systems help users discover relevant content, products, or services tailored to their individual interests and needs. Whether recommending movies, music, books, or products, these systems enhance user experience by minimizing the effort required to find relevant information or items. Additionally, recommendation systems benefit businesses by increasing customer engagement, satisfaction, and sales through targeted recommendations. The recommendation systems play a crucial role in optimizing decision-making processes and enhancing user satisfaction in various domains. This paper introduces a novel hybrid teaching model for college English, integrating the Collaborative Filtering Recommendation Algorithm with Hybrid Ant Whale Optimization Collaborative Filtering (HAWO-CF). The proposed model aims to personalize English language instruction by leveraging collaborative filtering techniques to recommend tailored learning materials and activities to students. Through simulated experiments and empirical validations, the efficacy of the HAWO-CF-enhanced hybrid teaching model is evaluated. Results demonstrate significant improvements in student engagement, comprehension, and learning outcomes compared to traditional methods. For instance, students using the HAWO-CF model exhibited a 25% increase in vocabulary retention and a 30% improvement in reading comprehension scores. Additionally, the model enabled instructors to optimize teaching strategies based on real-time feedback and performance data, leading to more effective and adaptive instruction. These findings underscore the potential of hybrid teaching models with HAWO-CF in revolutionizing college English education, and fostering personalized and impactful learning experiences.

Personalized Intelligent Recommendation Model Construction Based on Online Learning Behavior Features and CNN

The current intelligent recommendation models in online learning systems suffer from data sparsity and cold start problems. To address the data sparsity problem, a collaborative filtering recommendation algorithm model (SACM-CF) based on an automatic coding machine is proposed in the study. The model can extract the online learning behavior features of users and match these features with the learning resource features to improve the recommendation precision. For the cold-start problem, the study proposes a CBCNN model based on CNN, using the language model as the input of the model and the implicit factor as the output of the model. To avoid the problem of over-smoothing the implicit factor model, which affects the recommendation precision, an improved matrix decomposition method is proposed to constrain the output of the CNN and improve the model precision. The RMSE of SACM-CF is 0.844 and the MAE is 0.625. The MAE value of CBCNN is 0.72, the recall value is 0.65, the recommendation precision is 0.954 and the F1-score is 0.84. The metrics of SACM-CF and CBCNN are better than the existing state-of-the-art recommendation models. SACM-CF and CBCNN outperform the existing state-of-the-art intelligent recommendation models in all metrics. Therefore, the SACM-CF model and the CBCNN model can effectively improve the precision of the online learning system in recommending interesting learning resources to users, thus avoiding users' wasted learning time in searching and selecting learning resources and improving users' learning efficiency.

Collaborative filtering recommendation algorithm for e-commerce products based on bayesian network

Collaborative filtering recommendation algorithm for e-commerce products based on bayesian network

The Establishment and Practice of a Blended Teaching Model for Piano Music Education

Abstract This study explores a blended learning model for piano music education, merging traditional classroom instruction with an online personalized learning system to boost resource efficiency, student engagement, and learning outcomes. Utilizing the item collaborative filtering (CF) and learning style filtering recommendation algorithms, we tailored teaching materials to individual student needs, significantly improving match accuracy between resources and learners. Results from implementing this optimized hybrid teaching approach showed a 15% increase in course ratings, a 20% rise in student participation, and a marked enhancement in learners’ interest in piano studies. Additionally, learner satisfaction soared by 25% due to the learning style-based algorithm’s ability to personalize resource allocation. This research underscores the effectiveness of combining blended teaching models with personalized learning systems in elevating piano education quality and efficacy.

An E-Commerce Personalized Recommendation Algorithm Based on Multiple Social Relationships

Environmental e-commerce is a sustainability-oriented e-commerce model. To address the problem of data sparsity and the lack of diversity in traditional e-commerce recommendation algorithms, a new collaborative filtering recommendation algorithm based on multiple social relationships is proposed in environmental e-commerce. In real social networks, there were many relationships between users. On the basis of the traditional matrix decomposition model, the proposed algorithm integrates multiple social relationships between users into the user feature matrix, and then the multiple social relationships between users and the user rating preference similarity were used to jointly predict the user’s rating value for commodity, thus the personalized recommendation for users was achieved. In order to verify the superiority of the proposed algorithm, in this paper, two open datasets were used to compare the performance of several recommendation algorithms. The experimental results show that compared with the traditional social recommendation algorithms, the proposed algorithm improves recommendation accuracy and diversity. In real environmental e-commerce recommendation systems, the proposed algorithm can provide users with more personalized recommendation results, and reduce the arbitrariness of customer purchases and frequent returns in reality.

Retracted: Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

Retracted: Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

Research on Kindergarten Drama Education Activity System with Traditional Culture as Carrier under Big Data Platform

Abstract Drama education activities carried out by traditional culture comply with, guide and stimulate children’s dramatic nature so that each child can find a suitable place for himself in drama education activities and promote the growth of children with individuality. This paper builds a flipped classroom teaching model for kindergarten drama education activities based on the microblogging public platform. Teachers in the flipped classroom of drama education activities need to collect traditional cultural drama resources before class, and the basic framework of a personalized drama resource recommendation system is proposed on the WeChat public platform. The optimization of drama resource recommendation results is carried out using the preference score of drama resources using the collaborative filtering recommendation algorithm of multi-featured users. The performance test is conducted for the personalized drama resource recommendation system constructed in this paper to provide drama resources for the development of kindergarten drama education activities in the flipped classroom. The results show that when the preference recommendation of multi-featured users’ drama resources has a certain number of users, its accuracy is about 4.5% higher than that of the traditional method, the F-score value is about 0.05 higher than that of the traditional recommendation method, and the real-time updating delay of the personalized recommendation system fluctuates between 1.18s~1.65s. The personalized recommendation system can help teachers to obtain the update and download of traditional culture drama resources in time, make a good guarantee of drama teaching resources for kindergartens to carry out drama education activities with traditional culture as the carrier, and cultivate young children’s awareness of the protection of traditional culture.

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.

An analysis of the effects of a physical education web-based instructional program on college students exercising at home during COVID-19

Abstract Teaching online physical education courses under the epidemic normalization plays a positive role in students’ physical fitness. In this paper, taking 308 college students of X Sports College as a research case, personalized recommendation of physical education online teaching courses is carried out through a user collaborative filtering recommendation algorithm based on fuzzy clustering and user interests. The model of sports skill action development is constructed based on the mountain peak model and hourglass model, and the exercise intensity of the initial exercise is modified according to the results of home exercise intensity adjustment to obtain a personalized home exercise program suitable for individuals. Finally, the effectiveness of the home exercise optimization program was verified through the analysis of teaching constraints and exercise effects. The results show that under the epidemic normality, the difference between the demonstration movements and the test results in the 1st, 2nd and 3rd optimized teaching phases is between ±5.86, which is closer to the value of each index of the demonstration movements, indicating that the optimization and adjustment scheme of the home exercise of the physical education network teaching course has good effects. This paper has reference value for universities to conduct online teaching and training of university physical education courses in the context of major public health emergencies.

Optimization of intelligent recommendation of innovation and entrepreneurship projects based on collaborative filtering algorithm

The advent of the information age has made accurate search for information a challenge. In this paper, we analyze intelligent recommendations for innovative entrepreneurial projects based on collaborative filtering algorithms. Collaborative filtering is one of the most widely used and successful techniques in recommendation systems. In this paper, an interest migration function plus time is introduced to address the shortcomings of traditional collaborative filtering recommendation algorithms. Meanwhile, this paper builds an intelligent recommendation engine system for innovative entrepreneurial projects based on the Hadoop open-source distributed computing framework, sustainable PSCM, and Mahout collaborative filtering recommendation engine technology. This paper uses experiments to test and evaluate the overall performance of the distributed recommendation platform and the improved collaborative filtering recommendation algorithm. It is found that the algorithm outperforms similar algorithms in terms of data volume and coverage of recommended innovation and entrepreneurship projects. This is sufficient to show that the collaborative filtering algorithm and sustainable PSCM are useful for the intelligent recommendation analysis of innovative entrepreneurial projects.

A Collaborative Filtering Recommendation Algorithm Model Based on User Feature Transfer

The quality of teaching resources and recommended practices in flipped classrooms determine the advantages and disadvantages of this educational model. However, current teaching resource recommendation platforms suffer from issues such as cold startup and sparse data. In order to address these issues, a collaborative filtering recommendation algorithm based on user feature transfer was proposed and utilized to examine the correlation between students’ preference behavior and the recommendation of teaching resources in flipped classes for tourism management. The aim was to enhance the quality of teaching in current classes. Using this algorithm, a transfer by user feature (TUF) model is constructed. In this model, a soft embedding method is employed to accurately extract user feature information in the auxiliary space. This method addresses the challenge of filling large-scale missing matrices in the auxiliary space. Additionally, singular value decomposition is performed to obtain more precise user features. Finally, an optimized Wiberg algorithm with rapid convergence is designed to solve the constructed model. The performance of this algorithm is measured and compared using two common datasets. Compared to other algorithms, the proposed algorithm demonstrates superior performance and a lower mean absolute error value. In summary, the recommendation algorithm can enhance the quality of resource organization and provide more suitable personalized recommendations for the recommendation platform. This is beneficial for improving the quality of students’ learning.

Impact of Trust Adjustment Factor on Artificial Intelligence Driven Collaborative Filtering Recommendation Algorithm

Trust on artificial intelligence (AI) is a major concern in the contemporary computing paradigms. Studies show that AI systems may outsmart humans, leading to an ultimate extinction of mankind. Therefore, the behavior of these systems must be controlled to avert potential use by bad actors. Recommender systems, which are variant of AI products, learn shoppers past data and predict items that shoppers may prefer. This helps in identifying items that may be recommended to the active user. Studies indicate that classical recommender systems allow untrustworthy data, tempting unscrupulous dealers to misdirect the learning process. This action potentially defrauds buyers. Our study introduces trust adjustment factor into the AI learning pipeline. We conducted experiments to test the difference in robustness of the trust-enhanced collaborative filtering recommendation algorithm against the classical counterpart. Prediction shift and hit ratios for the two sets of algorithms were measured when subjected to various forms of profile injection attacks. We found that the trust-enhanced variant of the algorithm significantly outperforms classical collaborative filtering recommendation in terms of robustness by up to 52% when measured by prediction shift and by up to 18% when measured by hit ratio. Confirmed by t-test, results suggest that embedding trust adjustment factor into recommender systems improves its robustness.

A Study of Digital Museum Collection Recommendation Algorithm Based on Improved Fuzzy Clustering Algorithm

With the rapid advancement of internet technology, various industries have accumulated vast amounts of data, including on user behavior and personal preferences. Traditional museums can leverage this user data to uncover individual preferences and offer personalized services to their visitors. However, the exponential growth of information has also led to the problem of information overload, making it challenging for users to find relevant information within the vast data landscape. Consequently, the utilization rate of available information decreases. By harnessing the power of cloud computing, big data analytics, and recommendation systems, museums can enhance visitors’ touring experiences by helping them discover collections aligned with their interests and connecting with like-minded individuals. To address this objective, the research focuses on optimizing the initial clustering centers of the fuzzy clustering algorithm and parallelizing the optimized algorithm using MapReduce, resulting in the development of a novel MapReduce-based k-prototype fuzzy c-means (MRKPFCM) algorithm. Subsequently, the MRKPFCM algorithm is combined with the classical collaborative filtering algorithm to create a hybrid and parallelized collaborative filtering recommendation algorithm, incorporating elements such as MRKPFCM, audience, and collection. This hybrid algorithm is further supplemented by a content-based recommendation approach to generate comprehensive and refined recommendation results. Experimental findings demonstrate that the predictive scoring errors, as measured by RMSE and MAE, exhibited a downward trend when the number of nearest neighbors for target users fell within the range of 10–20. For instance, the studied algorithm’s MAE value decreased from 0.7512 to 0.7179, surpassing the corresponding figures for the two comparison algorithms. Moreover, with an increase in the number of nearest neighbors within the same range, all three algorithms experienced improved accuracy in prediction results. In particular, the accuracy rate rose from 17.84% to 18.82%, outperforming the two comparison algorithms. In summary, the enhanced hybrid recommendation algorithm achieved through this study displays superior recommendation accuracy and holds significant practical value.

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.

Research on the Optimization Pathways of University Education and Teaching Reform based on OBE Concepts

Abstract This paper proposes to introduce OBE theory in the curriculum map so as to build a curriculum master map based on the concept of engineering education accreditation-oriented and professional training objectives. In order to provide personalized services to students in the curriculum map, this paper applies the user-based collaborative filtering recommendation algorithm to the curriculum map and uses a similarity calculation based on Barclay’s coefficient improved similarity (BCCF) to finally implement the curriculum recommendation algorithm. The results show that the AUC of the recommendation algorithm constructed in this paper reaches 0.867. After the teaching reform, the mean values of students in the course reform class are 0.68, 0.61, and 1.31 higher than those in the non-course reform class at the level, of course, affect perception, teacher teaching, and student learning. Therefore, the OBE-based teaching method in this paper can develop personalized learning paths for students, which helps students learn better and plan the overall learning paths and make the most of educational resources.

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.

Innovation of fresh agricultural products e-commerce operation mode based on artificial intelligence and machine learning model

Abstract In recent years, there has been an increasing interest in emotional interaction models based on artificial intelligence technology, and researchers want to deal with real-life problems in daily life through artificial intelligence and emotional interaction technology. In this paper, based on artificial intelligence and the emotional interaction model, we analyze the model architecture of emotional interaction technology and the theory related to the e-commerce operation platform. Combining the two, we propose a collaborative filtering recommendation algorithm, define system performance indexes, and recommend fresh produce to target customers with similar users’ evaluation of fresh produce through artificial intelligence technology. Using the experiments of three different recommendation systems and user behavior data of different months in the past three years as reference, it is concluded that the e-commerce operation of fresh produce based on AI and emotional interaction model can effectively improve the browsing and transaction volume of the e-commerce platform. This shows that artificial intelligence and emotional interaction model can be applied to create a new e-commerce operation mode, which provides research directions and methods for the diversified development of future e-commerce operation modes.