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International Conference on algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI)

作者： Li, Lin Dalian Univ Sci & Technol Dalian Peoples R China

ISBN: (数字)9781510651890

ISBN: (纸本)9781510651890;9781510651883

Currently, there is a distinct contradiction between the massive information and the lack of personalized information. In this case, the recommendation system has been widely used as an important technology to discover the potential interests of users and recommend the items of interest to the target users. Considering that the probability matrix decomposition model can show its prediction mechanism more clearly, in this paper, the probabilistic matrix factorization model is used in the matrix factorization method. Based on the probabilistic matrix factorization, a recommendation method framework considering both extreme rating behavior similarity and rating matrix information fusion is proposed. The framework integrates the local neighbor relationship of users into the global rating optimization process of matrix factorization, and thus improves the prediction accuracy and robustness of sparse data. Simulation results show that the proposed method reduces the MAE by 0.68%, 1.12%, 2.85% and 1.19% compared with the suboptimal method. It is proved that the proposed method can effectively implement long tail project recommendation and ensure high recommendation accuracy.

关键词： E-commerce Search engine Matrix factorization model collaborative filtering algorithm FPMF algorithm

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Research on MOOC Course Design for College Students Based on collaborative filtering algorithm 22

Research on MOOC Course Design for College Students Based on...

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Proceedings of the 2022 5th International Conference on E-Business, Information Management and Computer Science

作者： Yexuan Liu Wuhan Business University China

ISBN: (纸本)9781450397827

With the explosive development of MOOC online education, the number of online learners continues to increase. Aiming at the problems that MOOC online learners are not easy to find suitable courses in the massive course information, so that blind selection leads to low learning efficiency and poor learning effect, this paper uses the recommendation algorithm of collaborative filtering to design the courses in MOOCs Conducting applied research in actual teaching, a new MOOC education course recommendation method is proposed, which provides a reference for machine learning or deep learning.

关键词： online education MOOC collaborative filtering algorithm machine learning deep learning

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A Personalized Matching System for Management Teaching Resources Based on collaborative filtering algorithm

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2020年第13期15卷 207-220页

作者： Shi, Yongqing Yang, Xiaojiang Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Peoples R China Anhui Sci & Technol Univ Fengyang Peoples R China

To realize education informatization, it is highly necessary to recommend teaching resources to students that can enhance their learning interest and improve teaching quality. This paper develops a personalized matching system for management teaching resources based on collaborative filtering (CF) algorithm. Firstly, the authors set up a user interest model, designed the flow and algorithm for personalized matching, and improved the similarity calculation method. Next, a personalized recommendation algorithm was developed based on the CF, and a personalized matching engine was constructed with the aid of Apache Mahout. The experimental results show that the proposed CF algorithm can effectively improve the recommendation quality, and push personalized teaching resources to each user;the learners are highly satisfied with the personalized matching system. The research results shed new light on personalized recommendation of teaching resources, opening up a new way to education informatization.

关键词： collaborative filtering algorithm management teaching resources personalized matching

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collaborative filtering algorithm Based on Improved Similarity Calculation 15

Collaborative Filtering Algorithm Based on Improved Similari...

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15th International Conference on Computational Intelligence and Security (CIS)

作者： Wang, Zhihe Shi, Suping Du, Hui Wang, Shuyan Northwest Normal Univ Coll Comp Sci & Engn Lanzhou Peoples R China

ISBN: (纸本)9781728160924

With the increasing contradiction between the vast supply of information and the diversity needs of users, it is a wise choice to apply personalized recommendation to relevant areas. Among them, collaborative filtering algorithm is the basic algorithm in personalized recommendation, and similarity calculation is the core of the algorithm. The accuracy of its calculation has a great impact on the recommendation results. In order to solve the problem that the traditional similarity calculation results have great deviation and the recommended results are not ideal, a new similarity calculation method is proposed in this paper. According to the closer the rating time is, the higher the user's similarity is, and the time factor is incorporated into the proposed algorithm. At the same time, in order to avoid the improper contribution of active users and popular item to the similarity calculation, the penalty factor is added respectively. The experiment was carried out on MovieLens data set, and the experimental result showed that the proposed similarity method improved the accuracy of scoring prediction to some extent, and the recommendation effect was better than the traditional one.

关键词： Personalized recommendation collaborative filtering algorithm Similarity calculation Time factor Punishmentfactor Scoring prediction

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Research on DIY Personalized Customization of Lovers' Clothing in Wechat Mini Program using a collaborative filtering algorithm

Research on DIY Personalized Customization of Lovers' Clothi...

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作者： Long Yu Ai-Min Xiao College of Textiles and Clothing Xinjiang University

There are many factors affecting users' preference for lovers' clothing..The survey found that most new users face the problem of choosing clothing templates when buying DIY clothes for *** paper studies the recommendation scheme of personalized template for lovers' *** user registration information,clothing parameter type selection is *** on the analysis using a collaborative filtering algorithm,users with the highest similarity of clothing parameter information in the system are calculated,and DIY clothing template information not selected by target users in the user template library is *** paper adopts the popular micro program technology to realize the development of DIY personalized recommendation system for lovers' clothing.

关键词： collaborative filtering algorithm Couples' Clothing Personalized Customization Wechat Applets

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Image Recognition Based on Sparse Enhancement and collaborative filtering algorithm 5

Image Recognition Based on Sparse Enhancement and Collaborat...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Deng, Dingsheng Sichuan Univ Nationalities Kangding 626001 Sichuan Peoples R China

ISBN: (纸本)9781665423144

With the development of compressed sensing theory, a large number of image denoising, image fusion, image super-resolution and collaborative filtering algorithms based on sparse enhancement have been proposed, and they have been widely used. However, the current research on sparse enhancement and collaborative filtering algorithms that have been applied to various image recognition fields still needs improvement. In this regard, the purpose of this article is to study image recognition based on sparse enhancement and collaborative filtering algorithms. In this paper, the score prediction value of the traditional collaborative filtering algorithm is affected by the number of common items. It starts by reducing the sparsity of common scoring items in the closest neighbors, and includes the closest neighbor users who are similar to the target user but have not yet scored the target item. Finally, the global and local features of the image are extracted to construct global and local feature dictionaries. The feature vectors of the image are jointly coded on the corresponding feature dictionaries. In addition, the size of the contribution of different features is reflected in the coding domain through adaptive *** classification of the image will be recognized,which is determined by the overall coding error. The experimental results prove that the sparse representation and collaborative filtering algorithms are highly feasible. Both occluded and unoccluded image recognition experiments performed very well, and the recognition rate reached more than 90%. It is very helpful for the development of image recognition system in actual industrial production.

关键词： Sparse Enhancement Coding collaborative filtering algorithm Dictionary Learning Image Recognition Technology

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Dual Preference Matrix collaborative filtering algorithm and Its Application in Railway B2B E-commerce Platform 5

Dual Preference Matrix Collaborative Filtering Algorithm and...

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IEEE 5th International Conference on Computer and Communication Systems (ICCCS)

作者： Wu Yanhua Ma ZhiQiang Zhao Zhengyang Cheng Zhibo China Acad Railway Sci Corp Ltd Inst Comp Technol Beijing Peoples R China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161365

The enterprise users of the B2B e-commerce platform are different from the individual users of B2C, and their behavior has typical features of enterprise. Based on the original user-commodity matrix of collaborative filtering algorithm, the enterprise-category matrix which manifests the features of enterprise users is added to ameliorate and form a dual preference matrix concerning both user-commodity and enterprise-category relationship, thus can be applied to Railway B2B E-commerce platform. The experimental results indicate that the improved dual-preference matrix collaborative filtering algorithm has advantages over the original collaborative filtering algorithm and KNN algorithm in terms of accuracy, recall rate, coverage rate and degree of novelty, effectively solving the problem of cold start and improves the accuracy of recommending commodities towards inactive users by over 10%.

关键词： B2B platform intelligent recommendation dual-preference matrix collaborative filtering algorithm

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Development and application of micro course resources for pre-school education based on collaborative filtering algorithm

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Computers and Education: Artificial Intelligence 2025年 9卷

作者： Junjun Zhang School of Culture and Education City University of Zhengzhou Xinmi 452370 Henan China

To address the issues of uneven resource distribution and the challenge of meeting individual needs in pre-school education, a micro-lesson resource recommendation system based on collaborative filtering algorithms was developed. By integrating multi-source data, matrix decomposition, and a hybrid recommendation model, the system enhances both the accuracy and diversity of its recommendations. Feedback collected from 300 children and 451 parents indicated that the recommendation system increased understanding, knowledge mastery, learning interest, and parent satisfaction by 15 %, 20 %, 18 %, and 10 %, respectively. Additionally, the system's real-time capabilities and response speed were optimized, ensuring that users receive the necessary resources promptly. These findings demonstrate the system's effectiveness in improving the quality of pre-school education and user satisfaction, while also providing technical support for the digital advancement of pre-school education.

关键词： collaborative filtering algorithm Pre-school education Micro-course resources

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Application of collaborative filtering algorithm in Mathematical Expressions of User Personalized Information Recommendation

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第2期12卷 1446-1453页

作者： Qian, Yufeng Hubei Univ Technol Sch Sci Wuhan 430068 Peoples R China

In order to solve the shortcomings of the existing mathematical expressions of the user's personalized recommendation search and to increase the accuracy of the user's personalized information recommendation, in this study, the collaborative filtering algorithm, the related theory of fuzzy sets, and the evaluation methods were introduced at first. Then, the establishment of collaborative filtering model and working principle, user similarity measure, generation of mathematical expression recommendation list, design of mathematical expression recommendation model and implementation of function were introduced. Finally, the application of the collaborative filtering algorithm in the user's personalized information recommendation mathematical expression was verified by collecting data. The results showed that the recommendation mathematical expression model based on collaborative filtering algorithm had higher accuracy in collecting user personalized information, and the accuracy of the system was higher than other recommendation algorithms, which can better meet the individual needs of users. This research can provide a fast and convenient way for users to search for personalized information, and has a good guiding significance for the retrieval and recommendation of informational scientific and technical literature. (C) 2019 The Authors. Published by Atlantis Press SARL.

关键词： collaborative filtering algorithm User personalized information Mathematical expression User similarity measure

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Rating Prediction on Movie Recommendation System: collaborative filtering algorithm (CFA) vs. Dissymetrical Percentage collaborative filtering algorithm (DSPCFA) 3

Rating Prediction on Movie Recommendation System: Collaborat...

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3rd International Conference on Informatics and Computational Sciences (ICICoS)

作者： Purnomo, Johan Eko Endah, Sukmawati Nur Univ Diponegoro Dept Comp Sci Semarang Indonesia

ISBN: (纸本)9781728146102

Recommendation system is one of many solutions for getting information rapidly from the many data available and one of its applications is the movie recommendation system. Movie recommendation system filters information then recommends movies based on rating preferences or user information. One of the most widely used algorithms is the user based collaborative filtering algorithm (CFA) to predict movie ratings which will be recommended based on similarity between target user and other users regardless of common items or the number of movies that have been rated by both. One different approach of the CFA algorithm is a dissymmetrical percentage collaborative filtering algorithm (DSPCFA) that involves common items as a consideration of measuring similarity. This study also uses two similarity measurement methods, namely the pearson correlation similarity method and the cosine similarity method as a comparison to determine the characteristics of each measurement method. The experiment results show that the DSPCFA algorithm produces a lower error value than the CFA algorithm with an error decrease of about 5% for the RMSE evaluation method (Root-mean Squared Error) and an error decrease of about 7% using the MAE (Mean Absolute Error) evaluation method. While measurement method tested shows that the pearson correlation similarity method produces a lower error value than the cosine similarity method.

关键词： movie reccomendation system collaborative filtering algorithm dissymmetrical percentage collaborative filtering algorithm common items

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