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Personalised recommendation method for MOOC online educational resources: collaborative filtering algorithm

INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2025年第1-2期35卷 156-170页

作者： Fan, Min Hangzhou Vocat & Tech Coll Hangzhou 310018 Peoples R China

In order to address the practical challenges associated with the low recall rate, accuracy, and user satisfaction in conventional personalised recommendation approaches for educational materials, a personalised recommendation method for MOOC online educational resources based on collaborative filtering algorithm is proposed. The mean shift clustering algorithm is used for clustering processing of MOOC platform data, and the MFA algorithm is used to dimensionality reduction process the clustering results. Learner interest are determined by considering the label preference based on frequency weight, time weight, comprehensive frequency and time, as well as learners' interest preferences for label clusters. Based on the analysis of learner interest, personalised recommendation of MOOC online education resources is achieved using a collaborative filtering algorithm based on semi-supervised learning. Experimental results show that the maximum recall rate of this method is 98.3%, the maximum recommendation accuracy is 97.8%, the mean learner satisfaction is 97.95, indicating good recommendation effectiveness.

关键词： collaborative filtering algorithm MOOC online educational resources personalised recommendation semi-supervised learning

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Application of big data search based on collaborative filtering algorithm in cross-border e-commerce product recommendation

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SOFT COMPUTING 2023年 1-9页

作者： Wu, Xiaoli Wu, Zhihao Yiwu Ind & Commercial Coll Sch Foreign Studies Jinhua 322000 Peoples R China Univ Lille Univ Polytech Hauts De France CNRS Cent Lille F-59000 Lille France

With the deepening of cross-border e-commerce, the trend of buying and selling goods through the Internet is rising. It is necessary to establish a cross-border e-commerce platform that meets the above functions, and improve the ability to process big data in search. For example, the emergence of large amounts of data can not only help users make choices, but also increase the difficulty of users in choosing. At present, there are many problems in the big data search system in the market, such as inaccurate user personality analysis and low importance of product recommendation. E-commerce is developing rapidly in the new era, and new users are increasing every day. Many researchers invest in finding excellent cross-border e-commerce recommendation system as a business platform. The number of information in cross-border e-commerce shows a rapid growth pattern, and the rapid growth of data and information has seriously affected people's judgment. The big data search system based on collaborative filtering algorithm can meet the product recommendation system of cross-border e-commerce. The user matrix label is an attribute of construction. For the label quantification, the new user preference is the model of building the label, and the concept of weight is added to the label. The collaborative filtering algorithm works based on the created weight label.

关键词： collaborative filtering algorithm Big data search Cross-border e-commerce Product recommendation system

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EVALUATION OF CURRICULUM IMPLEMENTATION EFFECTIVENESS OF HIGHER VOCATIONAL EDUCATION BASED ON collaborative filtering algorithm

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2023年第3期24卷 379-388页

作者： Wang, Hao Li, Huiyan Tianjin Univ Sch Educ Tianjin 300072 Peoples R China Univ Sanya Sch Hlth Ind Management Sanya 572022 Peoples R China

The conventional evaluation method for the effectiveness of curriculum implementation mainly focuses on the complete orientation analysis of students' curriculum content, which does not reflect the value of the educational curriculum and affects the effectiveness of evaluation. Therefore, an evaluation method of curriculum implementation effectiveness of higher vocational education based on a collaborative filtering algorithm is designed. Identify the practical focus of evaluating the implementation of the higher vocational education curriculum and discover the educational curriculum's significance. Qualitative evaluation of curriculum implementation degree based on collaborative filtering algorithm, find out the hidden characteristics of curriculum implementation evaluation to effectively evaluate higher vocational education curriculum implementation. Using case analysis, it is verified that the method is more effective and can be applied in real life.

关键词： collaborative filtering algorithm Higher vocational education Curriculum implementation Effectiveness Evaluation method

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Application of collaborative filtering algorithm based Collaboration and collaborative Environment in the Inheritance of Traditional Ceramic Culture 24

Application of Collaborative Filtering Algorithm based Colla...

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1st International Conference on Image Processing Machine Learning and Pattern Recognition

作者： Yang, Shiyu Dankook Univ Yongin South Korea

ISBN: (纸本)9798400707032

Nowadays, the protection and inheritance of traditional culture are facing unprecedented challenges. Against this background, this study selects traditional ceramic culture with profound cultural deposits as the research object, aiming to explore how to utilize modern scientific and technological means, especially information technology, to promote and strengthen the inheritance of traditional culture. We use collaborative filtering algorithms to build a learning environment based on collaboration and cooperation, which analyzes and processes user behavioral data to recommend learners with similar interests or needs to collaborate with each other, thus improving learning efficiency and depth of cultural understanding. In the knowledge test score, it went from 65-70 at the beginning to nearly 80;and in the skill assessment score, it improved from below 75 to about 90. It is found that this approach not only effectively promotes communication and interaction among learners and increases learning motivation, but also enhances learners' understanding and skill mastery of ceramic culture in a relatively short period of time.

关键词： collaborative filtering algorithm Cooperative Environment Traditional Ceramic Cultural Heritage Data Training Recommender System

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Research on Personalized Recommendation System for E-Commerce Products Based on collaborative filtering algorithm 3

Research on Personalized Recommendation System for E-Commerc...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Li, Long Nanning College for Vocational Technology Nanning Guangxi China

ISBN: (纸本)9798350380989

Research from aspects such as user consumption habits can help merchants better understand consumers' consumption needs. This article takes a large e-commerce website as the research object and analyzes user shopping behavior by studying shopping data over the past year. A hybrid recommendation model based on product recommendation and friend recommendation is proposed to achieve personalized recommendations for enterprise groups. This paper calculates the user's location similarity based on the user's location and the distance between locations, and then weights it with the user's interest similarity to obtain a new user similarity. This paper predicts the products that users are likely to purchase this month. Taking the fast-moving consumer goods e-commerce platform as an example, an empirical analysis of the proposed method was conducted. The performance of the algorithm was simulated using offline experiments. Experiments show that compared with the collaborative filtering method based on cosine similarity, the accuracy of this method can be 3.74% higher, and the recall rate reaches 3.91%. This method cannot only improve the user's selection efficiency, but also improve the performance of collaborative filtering. © 2024 IEEE.

关键词： collaborative filtering algorithm E-commerce personalized recommendation system purchasing behavior analysis

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Research on Personalized Music Recommendation Model Based on Personal Emotion and collaborative filtering algorithm

Research on Personalized Music Recommendation Model Based on...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Wang, Yingqiang Serrano, Elcid A. School of Information Technology Mapua University Philippines School of Electronic Information Engineering Xi'an Siyuan University China

In today's digital age, music plays an important role in people's lives, but the current music recommendation system is mainly based on content, the use of collaborative filtering and other algorithms, can not according to the user's real-time emotional state, recommend suitable for the current mood of the music. This paper aims to design and implement a personalized music recommendation model based on personal emotional information and collaborative filtering algorithm. The model mainly includes two sub-models: the emotion and music selection tendency model and the music recommendation model based on collaborative filtering algorithm. By analyzing the user's emotional state and music preference, the model provides music recommendation services that are more in line with the user's psychological state, so as to improve the user experience and recommendation accuracy. The model designed in this paper can effectively solve the problem that personalized music recommendation cannot be performed according to the user's real-time emotion. At the same time, it can also be used as a reference for other personalized recommendation models. © 2024 Y. Wang & E. ***.

关键词： collaborative filtering algorithm personal emotion personalized music recommendation

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Application of collaborative filtering algorithm of Resource Management Software in Community Service 3

Application of Collaborative Filtering Algorithm of Resource...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Li, Qin Mianyang Teachers' College Sichuan Province Mianyang City621000 China

ISBN: (纸本)9798350319125

The role of resource management software in community service is very important, but there is a problem of inaccurate outcome evaluation. The similarity measurement algorithm cannot solve the resource management software problem in community service, and the evaluation is unreasonable. Therefore, this paper proposes a collaborative filtering algorithm for resource management software analysis. Firstly, the Pearson correlation coefficient theory is used to evaluate the resource management software, and the indicators are divided according to the requirements of the resource management software and reduced Distractions in resource management software. Then, Pearson's correlation coefficient theory forms a resource management software scheme for resource management software and synthesizes the results of resource management software Analyze. THERLOY shows that in the case of certain evaluation criteria, collaborative filtering algorithms serve the community The accuracy of resource management software and the time of resource management software are better than the similarity measurement algorithm. © 2023 IEEE.

关键词： collaborative filtering algorithm community service Pearson correlation coefficient theory Similarity measurement algorithm

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Network Resource Personalized Recommendation System Based on collaborative filtering algorithm 5th

Network Resource Personalized Recommendation System Based on...

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5th EAI International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Qiu, Gang Cheng, Jie Shandong Univ Sch Software Jinan 250101 Peoples R China Changji Univ Changji 831100 Peoples R China

ISBN: (纸本)9783030945510;9783030945503

Several existing personalized recommendation systems for network resources do not specifically classify user history information personalized classification criteria, resulting in a low degree of matching between system personalized recommendations and user interests. In order to improve the accuracy of personalized recommendations for network resources, we designed a collaborative filtering based algorithm-based personalized recommendation system for network resources. In the hardware design, design the overall circuit module, configure the bus timing, and improve the operating speed of the system hardware. In software design, calculate the time weight function to construct the user's implicit scoring matrix;calculate the similarity between network resources and user browsing history items, design personalized classification criteria for user history information based on collaborative filtering algorithms;calculate predicted item scores and actual scores Establish a personalized recommendation model for network *** the experiment, the system is compared with several existing systems, and the average absolute error in different adjacent sets is tested. According to the data results, in the five data test sets, the average absolute error of the system is less than that of other systems, so the personalized recommendation system based on collaborative filtering algorithm has better recommendation accuracy.

关键词： collaborative filtering algorithm Overall circuit module Bus timing Time weight function Recommendation model

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Analysis of collaborative filtering algorithm in Personalized Push Service of University Library

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Procedia Computer Science 2023年 228卷 502-510页

作者： Zhiyu Zhu

The personalized push service function of university library is beneficial to the library's role and can help students learn, but whether this function can achieve good results depends largely on the accuracy of push, and collaborative filtering algorithm can help improve the accuracy of push. Therefore, in order to use the collaborative filtering algorithm to do a good job in personalized push service, this paper will carry out relevant research, introduce the basic concepts of personalized push service and collaborative filtering algorithm, and then analyze the use of the algorithm in personalized push service, in order to provide reference help.

关键词： University Libraries Personalized Push Service collaborative filtering algorithm

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A Modified Memory-Based collaborative filtering algorithm based on a New User Similarity Measure 2

A Modified Memory-Based Collaborative Filtering Algorithm ba...

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2nd International Conference on Innovative Technology Convergence (CITC) - Embracing Change and Transformation - Post Pandemic Opportunities

作者： Lumauag, Ramil G. Iloilo Sci & Technol Univ Comp Dept Iloilo Philippines

ISBN: (纸本)9781665421928

Data sparsity remains to be a critical concern for recommendation systems since it results in low accuracy and poor recommendation quality. To address this problem, collaborative filtering techniques based on user similarity have been applied but existing implementations have not been shown to sufficiently address the problem of data sparsity. Thus, this paper presents an enhanced memory-based collaborative filtering algorithm utilizing a new similarity measure that identifies co-rated items and computes user similarity to overcome the data sparsity problem and improve the recommendation quality which can be adopted for various applications. Results of the study show that the use of the new similarity measure has improved the determination of user similarity than when using the traditional Cosine, Euclidean Distance, and Pearson Correlation similarity metrics.

关键词： collaborative filtering algorithm memory-based collaborative filtering recommender systems user smiliraity measure

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