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 recomm...
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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.
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 ...
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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.
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 ed...
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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.
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 resea...
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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.
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 shopp...
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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 collaborativefiltering and other algorithms, can not...
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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 softw...
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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 syst...
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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 collaborativefiltering 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.
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 p...
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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.
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, collaborativefiltering techniques based on user sim...
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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, collaborativefiltering 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.
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