Resource Recommendation System Research Articles

Design and Evaluation of a Learning Resource Recommendation System Based on Machine Learning

In the era of information overload, learners are often overwhelmed by the vast amount of educational content available online. To address this challenge, we have developed a Learning Resource Recommendation System (LRRS) that leverages machine learning techniques to provide personalized learning material suggestions. The LRRS is designed to analyze learners' profiles, preferences, and learning behaviors to deliver a tailored learning experience. By employing collaborative filtering, content-based filtering, and hybrid methods, the system can predict and recommend resources that are most relevant to individual learners. This paper presents the architecture of the LRRS, the methodologies used for recommendation, and the results of its evaluation. We conducted experiments using a dataset of learners and resources to assess the accuracy and effectiveness of the system. The evaluation metrics, including precision, recall, and F1-score, demonstrate the system's ability to enhance the learning process by providing relevant and engaging content. The LRRS also includes a feedback mechanism that allows for continuous improvement of the recommendation algorithms based on user interactions. The findings of this study contribute to the field of educational technology by offering insights into the development of intelligent systems that can support personalized learning.

An Innovative Demonstration of Functional Linguistic Ideas for Teaching English in Colleges and Universities Based on Multi-objective Optimization Models

Abstract Aiming at the current characteristics of English teaching in colleges and universities which are too theoretical, this paper combines the ideas of functional linguistics to innovate the teaching method, focusing on constructing a personalized English teaching resources recommendation system to assist English teaching. Among them, in the teaching resources recommendation module, the collaborative filtering algorithm is improved by calculating the learner trust degree and similarity degree, then describing the learner features and learning resources features respectively, constructing the multi-objective optimization function, and solving the optimal value by using the hybrid differential evolutionary algorithm. The constructed English teaching resources recommendation system is applied to actual teaching. For the answer situation of knowledge difficulty category 5, the highest level students can still maintain the average correct rate of 55.21%. In the comparison experiment of innovation effect demonstration, the experimental group and the control group reached statistical significance in fluency index (P=0.038<0.05) and logic index (P=0.001<0.05). This study can promote the practicability of English teaching and increase the effect of cultivating applied talents.

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.

Computer Aided Music Instructional Resource Recommendation System Based on Knowledge Map and Collaborative Filtering

Computer Aided Music Instructional Resource Recommendation System Based on Knowledge Map and Collaborative Filtering

Retracted: Design of Personalized Ideological and Political Education Resource Recommendation System in the Online Education Platform in the Network Environment.

[This retracts the article DOI: 10.1155/2022/3845749.].

Retracted: An English Teaching Resource Recommendation System Based on Network Behavior Analysis

Retracted: An English Teaching Resource Recommendation System Based on Network Behavior Analysis

Design and Implementation of a Learning Resource Recommendation System based on User Habits Based on GNN

This project aims to design and implement a learning resource recommendation system based on Graph Neural Networks (GNN). The system utilizes user learning habits as a foundation to provide personalized learning resource recommendations. By collecting and preprocessing user learning history data, and constructing a user-resource relationship graph, the GNN model is used to learn the representation vectors of users and resources. Combined with user habit features, appropriate recommendation algorithms are employed to recommend learning resources that align with their interests and habits.

A Study on the Recommendation Method of Intelligent Media Learning Resources in the Foreign Communication and Teaching of International Communication of Chinese Central Plains Culture

Today, with the development of intelligent media, the foreign communication and teaching activities of the Chinese central plains culture should actively seek experiences that can be learned from, establish a multi-channel foreign communication mode, and then promote the Chinese central plains culture to go out of the country and into the world better. The study improves the collaborative filtering recommendation algorithm and the joint matrix decomposition algorithm based on the theory of migration learning, aiming to improve the learning to optimize the resource recommendation system by calculating the user similarity and establishing the user preference-resource feature matrix. The experimental results show that the average absolute error and root mean square error of the improved algorithms are lower than those of other algorithms, proving that the optimized algorithms improve the accuracy and efficiency of resource recommendation in the foreign communication and teaching activities of the Chinese central plains culture while operating stably and with wide applicability on the recommendation system.

Research on Media resource recommendation System and recommendation Technology based on Graph Neural network

In the field of artificial intelligence research, personalized recommendation technology has been rapidly developed. It can not only provide effective recommendation according to the preferences of system users, but also sort out the historical behavior and attribute information of users, and accurately calculate the content of user preferences. From the perspective of the development of the new media industry, in order to better meet the resource needs of system users, a resource recommendation system based on personalized recommendation technology can fully meet their needs for media reading. Therefore, on the basis of understanding the current research status of media resource recommendation system and technology, and according to the basic concept of graph neural network and the main structure of recommendation system, this paper mainly studies the application effect of the recommendation model of outburst neural network that integrates knowledge graph and time weight, so as to provide effective basis for media resource recommendation in the new era.

The Design of Personalized Learning Resource Recommendation System for Ideological and Political Courses

Colleges and universities increasingly incorporate ideological and political (IP) concepts into their courses as a fundamental prerequisite and a rising IP education trend under changing conditions. Students have difficulty sifting through the ever-growing amount of online information to locate what they need in learning resources. Technology-enhanced learning encompasses any technology that helps students study more effectively. This paper suggests a personalized learning resource recommendation system (PLRRS) for IPC. Personal learning recommendation systems (PLRSs) that do their task well will help students cope with the existing information overload. They will make sure that they receive the correct information at the right time and in the right format for their particular needs. E-learning systems that intentionally personalize their courses to the preferences, objectives, skills, and interests of the students they serve are engaging in personalized learning. In the last several years, researchers have been looking at ways to assist instructors in enhancing e-learning. Personalized learning scenarios are created by picking the most relevant learning objects based on an individual’s profile. A test score greatly improved for students in IPC after using the model in this research, which suggests that this model has a strong promotion value.

Design of Data Persistence for Network Resources Recommendation System Based on Hibernate Architecture

The Python language is more focused on problem solving, which is in line with the era of computational thinking, and the teaching of Python language course requires students to systematically master the basic concepts, programming ideas and programming techniques of Python, and have the idea of object-oriented software design technology. We have developed a recommended system of online resources for "Python Programming" course to solve the problem of students' access to resources and deepen the teaching reform of the Python Programming course. Data persistence is an important task in system development, and Hibernate is the most popular O/R Mapping framework. The data persistence design based on Hibernate solves the key technical problems in the development of the network resource recommendation system for the "Python Programming" course, and improves the efficiency and maintainability of the software system development.

Research on educational resource recommendation system based on MRLG Rec

Abstract Internet information capacity is expanding and developing at a high speed every day. The huge amount of information data involves many problems, such as resources could be scattered, overlapped and confused and thus difficult for users to timely and accurately capture information suitable for them, especially in the field of education. Through the MRLG Rec algorithm, the following processes can be carried out: deep processing, characterisation of data to generate unique individual portraits of user information and to store education resource databases at the same time and analysis of recommendation system to provide users with more accurate high-quality education resources, thus forming a two-way unblocked and efficient information transmission closed-loop, which help learners find teaching resources of the time. It also has screening channels of high-quality educational resources. Through the analysis of experimental data, it is concluded that the algorithm improves the user viscosity of human-computer interaction in the teaching resource recommendation system.

Machine Learning-Driven Enterprise Human Resource Management Optimization and Its Application.

With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resource information, such as talent information and job information, has also increased unprecedentedly, which makes human resource services face information overload. Especially with the gradual increase of the amount of information, this method is not enough for the acquisition and classification of massive data. After that, experts developed search engines to deal with the retrieval problem, and the first ones were Google and Baidu. As long as the search engine is clear about the direction of the search, it is indeed very convenient for the retrieval of massive data. However, in many cases, most users cannot clearly recognize the content they need or how to accurately express their needs. Faced with this problem, people propose recommender systems to solve the problem of obtaining preference information, which can better increase the user's experience and meet their own needs more easily. Based on the main workflow of the recommender system, this paper designs the overall architecture of the human resources recommendation system and implements a human resources recommendation prototype system based on deep learning. The system can better overcome the cold start problem and provide real-time recommendation results, improving the quality of HR personalized recommendation results.

A Recommendation Model for College English Digital Teaching Resources Using Collaborative Filtering and Few-Shot Learning Technology

This study designs and implements a digital English instructional resource management recommendation system based on collaborative filtering technology based on CF research and the construction of digital English instructional resources. This study designs the system with B/S structure combined with hierarchical design architecture, plans the overall design goal, architecture design, compilation structure, and key technologies, and designs and implements the system's core modules on the basis of fully analysing the functional and nonfunctional requirements of personalized educational resource recommendation system. Furthermore, the traditional CF has been improved to address scalability, data sparseness, and user cold start in the recommendation process. The evaluation results show that this algorithm has a recall rate of 96.37% and a system resource recommendation accuracy rate of 95.31%, both of which are higher than the traditional method's 6.37%. This algorithm can overcome the drawbacks of traditional algorithms, improve recommendation accuracy, and efficiently provide high-quality English teaching resources. Educators and students will be more likely to find high-quality digital English instructional resources if they use the instructional resource system proposed in this study.

The Design of Personalized Education Resource Recommendation System under Big Data.

With the advent of the Internet and the era of big data, education is increasingly dependent on data resources to support product and business innovation, and the lack of data resources has severely limited the areas involved. As a general information filtering method, personalized recommendation systems analyze the historical interaction data between users and items to build user interest models in an environment of “information overload”, allowing users to discover and recommend information that interests them. However, the explosive growth of information in the network makes users wander in the sea of information, and it is increasingly difficult to find the information they really need, i.e., information overload. This has given rise to personalized recommendation systems, which currently have more mature applications in industries such as e-commerce, music services, and movie services. To this end, this paper studies and implements a customized educational resource recommendation system that can handle big data. The results show that the values of different similarity calculations all fluctuate with the gradual increase of the number of nearest neighbors, and the algorithm in this paper is maximum at the number of neighbors around 60; then, it is inferred that applying the calculation method to the recommendation algorithm will improve the recommendation accuracy. Therefore, education uses the concept of big data to process the huge amount of education data and find some correlations and laws in education, so as to realize “teaching according to the material, teaching according to the material”.

Application of Conditional Random Field Model Based on Machine Learning in Online and Offline Integrated Educational Resource Recommendation

It is of great significance to mine the learning resources that learners are interested in from massive data and recommend appropriate educational resources to them according to the characteristics of students. To improve the accuracy of educational resource recommendation, this paper proposes an educational resource recommendation system based on a graph attention network and conditional random field fusion model. It builds all comments for each student and educational resource into a comment graph. Through the graph's topological structure to capture the network and the dependency between words in the commentary text, the adjacency information of each node is aggregated by the graph attention network based on connection relation. After the graph attention network layer, the conditional random field inference layer is added. The label sequence with the highest probability is output by the dependent random field inference layer, which is taken as the final recommendation result of the model. Experimental results show that the proposed algorithm has better performance in accuracy and diversity than the traditional recommendation algorithm.

A Study on E-Learning and Recommendation System

Introduction: Today’s technology and the internet are proliferating due to which information access is becoming easier for many people, and creating new challenges and opportunities in all fields, especially when working with education. For example, the e-learning education system can personalized in order to acquire knowledge level and learner’s requirements in a learning process. The learning experience, as per the individual learner’s goals, should be adopted. Background: In the current educational environment, e-learning is playing a significant role. For many researchers, it has become one of the most important subjects, as using e-learning whole education system would revolutionize. There are many areas of e-learning in which research work is going on, such as Mass Communication, Information and Technology (IT), Education and Distance Education. Objective: To meet the various needs of the learners such as talents, interests, goals and needs, an e-learning system has to design a personalized learning system by considering various educational experiences. Many methods such as ontologies, clustering, classification and association rules have used along with filtering techniques to enhance the personalization and performance of the learner. Methods: This paper presents a detailed review of literature of previous work that has done in e-learning area especially on recommendation system. Current research works on e-learning to discover the research developments in this discipline have discussed in this work. Conclusion: One of the vital functions of the current e-learning system is creating a personalized resource recommendation system. In this paper, we reviewed some crucial papers on both e-learning and recommendation systems. Future research work of this paper would be designing efficient and precise e-learning and recommendation system to deal with the problem of substantial personalized information resources and further e-Learning plays vital role in preventing virus spread during COVID-19 pandemic.

The Impact of Digital Technology on the Optimization of Higher Education Teaching Models in an Epidemic Environment

The Corona Virus Disease 2019 epidemic broke out in 2020, and digital technologies pervaded all aspects of people’s lives, resulting in a significant shift in how education is delivered. The importance and role of digital technologies and online learning are highlighted in this paper, which examines the challenges posed by the sudden epidemic crisis to higher education institutions, analyses the factors that universities must consider in order to effectively create flexible learning pathways, and examines the challenges posed by the sudden epidemic crisis to higher education institutions. In the postepidemic era, the use of the Internet and online teaching platforms by university faculty to integrate online and offline teaching has not only facilitated the construction of “golden courses” but also added impetus to teaching reform, and digital technology-based teaching models have provided higher education practitioners with the opportunity to rethink scholarship and innovative teaching. In this paper, we propose a personalized learning resource recommendation system that includes user profiles to fully explore and analyze users’ learning behaviors and cognitive characteristics and enhance the depth and breadth of personalized education with the help of the Internet and artificial intelligence technologies in order to provide meaningful information and thoughts for higher education institutions to discuss and adapt to the education model in the postepidemic era. The goal is to provide useful information and ideas for higher education institutions to discuss and adapt to the postepidemic education paradigm.

Research on University Innovation and Entrepreneurship Resource Database System Based on SSH2

With the wide application of the Internet, the entrepreneurial resources of colleges and universities have grown at an exponential rate. With the rapid accumulation of this information, it is difficult for students to find what they are interested in from a large amount of information. To accurately recommend innovation and entrepreneurship resources, this paper proposes a recommendation algorithm based on user trust and a probability matrix. After obtaining the user trust data, the PMD (probability matrix decomposition) algorithm is used to complete the trust matrix and normalize it to get the similarity matrix. At the same time, the trust factor between users is added to the calculation process of the posterior probability, and the prediction score is obtained by maximizing the posterior probability. On this basis, the weights of users in the group are normalized, and the weighting strategy based on user interaction is used to integrate the preferences of group members to obtain the final recommendation results. When designing the recommendation system, the web system of the mainstream SSH2 framework is used to design, and the B/S structure of the entrepreneurial resource recommendation system platform is realized. Experimental results show that the proposed system has a higher recommendation quality compared with other recommended algorithms.

Research on Deep Learning-Based Algorithm and Model for Personalized Recommendation of Resources

Resource recommendation system is a new type of management system, which uses personalized information to solve business needs such as customer consultation and product recommendation, and provides users with high quality services and achieves accurate marketing, so nowadays resource recommendation system has a pivotal role in modern resource management. In this paper, I study the algorithm and model of resource personalized recommendation based on deep learning, taking human resource recommendation as an example.