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Research on the innovation theory of college students' ideological and political education under the background of big data

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Lai, Wenling Wuchang Univ Technol Tongshi Coll Qual Educ Wuhan 430223 Hubei Peoples R China

In order to improve the ideological and political education of college students, this paper constructs a cloud technology education platform based on big data analysis and collection technology. Firstly, the collaborative filtering algorithm is used to filter and collect student information;secondly, the Pearson correlation formula is used for pre-processing, MAE (mean absolute error value) is used as an evaluation index;and finally, the data is combined with FCM (fuzzy C-mean) algorithm to filter and analyze as needed. Its application performance is examined to verify the rationality and practicality of the construction of the cloud technology education platform. The analysis results show that, compared with the three-tier neural network education platform and MOOC (Massive Open Online Course) education platform, the educational resource transmission time of the cloud technology education platform constructed in this paper is reduced by 1/3, and the load balancing deviation is reduced by 1/4. Among four different servers, the average error value of the platform constructed in this paper is as low as 0.35% and can reach as high as 0.65%. The detection rate reaches 97.64%, the false alarm rate is only 2.93%, and the leakage rate is only 1.13%. It can be seen that the cloud technology education platform constructed in this paper can improve the utilization rate of educational resources and realize the sharing of educational resources.

关键词： Big data collection and analysis technology Cloud technology education platform FCM algorithm collaborative filtering algorithm Pearson's correlation calculation formula

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Agricultural Product Recommendation Model and E-Commerce System based on CFR algorithm 2

Agricultural Product Recommendation Model and E-Commerce Sys...

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2nd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Chen, Tungchun Liang, Yushen Huang, Tienshou Huang, Juichan Liu, Chengju National Pingtung University of Science and Technology Department of Plant Industry Pingtung 91201 Taiwan National Kaohsiung University of Science and Technology Department of International Business Kaohsiung 82444 Taiwan National Kaohsiung University of Science and Technology Department of Intelligent Commerce Kaohsiung 80778 Taiwan Yango University Fuzhou 350015 China

ISBN: (数字)9781728181158

ISBN: (纸本)9781665495271

With the development of e-commerce, people have more pursuit for online shopping. In the online shopping environment, users also need a good experience when browsing goods online. So now more and more people choose to buy their favorite agricultural products with certain characteristics, affordable and reassuring through the Internet. Therefore, the research on agricultural product recommendation model and e-commerce system design based on collaborative filtering recommendation (CFR) algorithm is very useful. Firstly, this paper introduces the characteristics of agricultural product recommendation, then studies the application of collaborative filtering algorithm, designs and develops the agricultural product recommendation model and e-commerce system based on the algorithm, and tests the function of the model. Finally, the test results show that the short-term prediction accuracy of the system is slightly low, but the accuracy is relatively high, covering a wide range of agricultural products, and the user coverage is also high, which can meet the shopping needs of users for agricultural products. © 2022 IEEE.

关键词： Agricultural Product Recommendation collaborative filtering algorithm E-commerce system Recommendation Model

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Research on Short Video Recommendation algorithm Based on Social Network

Research on Short Video Recommendation Algorithm Based on So...

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

作者： Hang, Zixiao Fairmont Preparatory Acad 2200 W Sequoia Ave Anaheim CA 92801 USA

ISBN: (数字)9781510651890

ISBN: (纸本)9781510651890;9781510651883

According to the rapid development of the technological age and the rapid advancement of databases, it will be very difficult to find out the information they need in the social application and also consume a lot of time. Therefore, humancreated Recommendation System. Large and small video firms, such as Douyin, Instagram, and Facebook, employ the recommendation system to allow everyone to browse their favorite material. However, as time passes and more people's needs change, large data makes it harder to convey really useful information to users, and major issues such as information overload have arisen regularly. There are currently only two solutions. The first is search engines, which are a highly prevalent information retrieval method, and the second is recommendation systems, which are the most effective information filtering technology. In the background of short video software, the collaborative filtering algorithm is the most used recommendation system.

关键词： Recommendation System collaborative filtering algorithm Information filtering Technology

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Hesitant Fuzzy Linguistic Correlation Coefficient and Its Applications in Group Decision Making

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第6期22卷 1748-1759页

作者： Li, Shengli Wei, Cuiping Taiyuan Normal Univ Dept Math Jinzhong 030619 Peoples R China Yangzhou Univ Coll Math Sci Yangzhou 225002 Jiangsu Peoples R China

Hesitant fuzzy linguistic term sets (HFLTSs) are applied to deal with situations in which people are hesitant in providing linguistic evaluations and have been widely used in qualitative group decision-making processes. Considering the fact that the correlation coefficient has been widely used in many research domains, in this paper, we first present a novel correlation coefficient for HFLTSs. The significant features of the proposed correlation coefficient are that the HFLTS s need not to be extended to the same length and the correlation coefficient lies in [-1, 1]. Two examples are used to illustrate the effectiveness of the proposed correlation coefficient. Second, based on the proposed correlation coefficient and the collaborative filtering algorithm, we develop a new method to deal with incomplete hesitant fuzzy linguistic information. Furthermore, by analyzing the correlation coefficient matrix, we present a new method for determining experts' weights based on the concept of 'attenuation' and 'how much trust' they obtained. At last, a case study is used to evaluate the performance of our method.

关键词： Group decision making correlation coefficient collaborative filtering algorithm hesitant fuzzy linguistic term sets trust relationship

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Research on Personalized Tourist Attraction Recommendation based on Tag and collaborative filtering 31

Research on Personalized Tourist Attraction Recommendation b...

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31st Chinese Control And Decision Conference (CCDC)

作者： Cui, Yanqing Huang, Chuanlin Wang, Yanping Dalian Neusoft Univ Sch Informat & Business Management Dalian 116023 Peoples R China

ISBN: (纸本)9781728101057

Tourists face a large number of tourist attractions, and spend a considerable amount of time and energy to select satisfactory tourist attractions. The application of personalized recommendation technology is an effective way to solve this problem. On the one hand, users' consumption frequency in tourism is much lower than other commodities such as music and movies;on the other hand, the increasing number of tourist attractions has led to the problem of sparse scoring data in personalized recommendations of tourist attractions. The traditional collaborative filtering algorithm is not satisfactory in the recommendation of tourist attractions. This paper builds a tourist attraction tag system, which links tourists and tourist attractions through the attractions tag from four aspects: location, location type, travel time, and travel method. By calculating the relationship between tourists and attractions tags, tourist attractions and attractions tags, a user interest model is constructed. Then, according to the user interest model, the interest degree of the new attraction to be recommended is predicted, and finally the tourist attraction recommendation set is generated.

关键词： Tag collaborative filtering algorithm Tourist attractions Personalized recommendation

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Trust -based collaborative filtering algorithm

Trust -based collaborative filtering algorithm

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5th International Symposium on Computational Intelligence and Design (ISCID)

作者： Xu, Xiaowei Wang, Fudong Donghua Univ Glorious Sun Sch Business & Management Shanghai Peoples R China

ISBN: (纸本)9780769548111

The rapid development of internet has brought us into the era of information explosion, which brings the widespread application of the personalized recommendation system. collaborative filtering recommendation algorithm is the most widely used algorithms in the personalized recommended system, but it faces problems like the data sparsity, cold start," idler" attack. With the development of social network, many e-commerce sites, social network sites introduce the trust mechanism, which becomes the new approach to overcome the problems of the traditional collaborative filtering algorithms. This paper first introduces the collaborative filtering recommendation algorithm and the existing problems, and then summarizes the current trust based collaborative filtering algorithms. For the trust based collaborative filtering algorithms, this paper summarizes the trust expressions and metrics, and focus on analyzing the representative trust models.

关键词： Personalized recommendation collaborative filtering algorithm trust

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Research on the Application of Curriculum Knowledge Point Recommendation algorithm Based on Learning Diagnosis Model 5

Research on the Application of Curriculum Knowledge Point Re...

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5th International Conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Gan, Baiqiang Zhang, Chi Guangzhou Nanyang Polytech Guangzhou 510900 Peoples R China Sun Yat Sen Univ Nanfang Coll Guangzhou 510900 Peoples R China

ISBN: (数字)9781728199283

ISBN: (纸本)9781728199283

Because the degree of mastering knowledge points in courses in traditional cognitive diagnostic models cannot be probabilistic, there are only two situations: mastery and non-mastery. Therefore, for the current research, the recommendations of knowledge points recommended by learners' learning behavior attributes are not fully considered to be insufficient, this paper proposes a curriculum knowledge point recommendation algorithm model based on learning diagnosis, the model comprehensively considers the learner's learning emotions, learner problem test conditions and knowledge point characteristics, and the film and television synthesis in the Chaoxing online teaching service platform the course learning data is tested to verify the effectiveness of the recommendation algorithm. The experimental results show that the effectiveness and accuracy of the recommendation algorithm model proposed in this paper can meet the learning needs of learners.

关键词： learning diagnosis model course knowledge points recommendation algorithm clustering algorithm collaborative filtering algorithm

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An Improved News Recommendation algorithm Based on Text Similarity 3

An Improved News Recommendation Algorithm Based on Text Simi...

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3rd International Conference on Smart BlockChain (IEEE SmartBlock)

作者： Gao, Yihang Zhao, Hui Zhou, Qian Qiu, Meikang Liu, Meiqin Henan Univ Henan Int Joint Lab Theories & Key Technol Intell Kaifeng Peoples R China Henan Univ Educ Informat Technol Lab Kaifeng Peoples R China Yellow River Water Conservancy Vocat & Tech Coll Art Dept Kaifeng Henan Peoples R China Texas A&M Univ Commerce Dept Comp Sci Commerce TX 75428 USA Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781665440738

With the advent of the data age, the public has been facing the problem of information overload. Recommendation algorithms are an effective way to solve this problem. At present, a large number of recommended algorithms adopt the following two ideas: content-based text similarity algorithm and user-based collaborative filtering algorithm. Researchers have developed a distributed collaborative recommendation protocol based on blockchain. However, these algorithms ignore the characteristics of the news industry itself. Just adopting the above ideas will inevitably lead to many internet public opinion problems. Therefore, this paper proposes an improved N-TF-IDF algorithm, which is more suitable for the news industry, and can control the outbreak of negative public opinion, and has a positive effect on stabilizing internet public opinion. Through the verification of the experimental data set, the algorithm is superior to the traditional information retrieval and text mining technology TF-IDF in both the time dimension and the emotional dimension, and this algorithm is not affected by citizens' privacy rights.

关键词： Information Overload Recommendation algorithm collaborative filtering algorithm Privacy-Preserving TF-IDF Blockchain

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Analysis of book circulation data and a book recommendation system in academic libraries using data mining techniques

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LIBRARY & INFORMATION SCIENCE RESEARCH 2022年第4期44卷

作者： Khademizadeh, Shahnaz Nematollahi, Zahra Danesh, Farshid Shahid Chamran Univ Ahvaz Fac Psychol & Educ Sci Dept Knowledge & Informat Sci Golestan Blvd Ahvaz *** Iran Reg Informat Ctr Sci & Technol RICeST Informat Management Dept Jame Jam AveIslamic Republ Blvd Shiraz 719469417 Iran

The use of data mining modern technology in library management systems and information centers is of great importance. With the increasing availability of a large quantity of information, traditional tools and practices without wasting time and cost cannot respond to users accurately and quickly. The present study aims to analyze book circulation transactions and discover the user's book loan patterns to develop a recommender system. The data included 109,639 transactions and information from 8636 user records. Microsoft SQL Server and Matlab software were applied to analyze the data. Item-based collaborative filtering algorithms and decision tree methods were also applied. The results led to the extraction of rules for suggesting books to users. Analysis of the circulation data could be applied to address many issues like evaluation, collection acquisition policies, allo-cating funding for materials, and suggesting approaches to deselecting and allocating physical space for materials.

关键词： Information resource circulation Recommendation system User access pattern Academic library Association rule collaborative filtering algorithm Data mining

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Study on Improvement of Recommendation algorithm Based on Emotional Polarity Classification 5

Study on Improvement of Recommendation Algorithm Based on Em...

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

作者： Cao, Hui Kang, Jian Northwest Minzu Univ Minist Educ Key Lab Chinas Ethn Languages & Informat Technol Lanzhou Gansu Peoples R China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161365

This paper expounds the research and improves the object-based collaborative filtering recommendation algorithm by using the emotional polarity classification results of user reviews. Taking the film recommendation system as an example, the recommendation algorithm is introduced. The CNN emotional polarity classification model is used to classify the emotional polarity of the movie review information. The process includes (Chinese word segmentation, de-stop words, feature extraction, and CNN classification model). The emotional polarity classification result of the comment is added to the parameter of the user movie rating, and a recommended movie list is generated according to each user's movie rating result.

关键词： recommendation algorithm collaborative filtering algorithm emotional polarity classification

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