Collaborative Filtering Algorithm based on Data Mixing and Filtering

Cheng, Xiaohui; Feng, Lei; Gui, Qiong

doi:10.23940/ijpe.19.08.p27.22672276

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…The CF algorithm is used in the study's algorithm section. Because the CF technique requires a lot of calculations when dealing with a big number of users and products [18,19,20], the clustering approach can partially address this issue. Using distance as a similarity metric-closer distances signifying greater similarity-the K-means algorithm is a common clustering algorithm.…”

Section: Trr Modelmentioning

confidence: 99%

Multiple Constraint Hybrid Travel Route Recommendation Model Based on Collaborative Filtering Recommendation Algorithm

Zhang

2024

SCPE

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The study presents a hybrid model for recommending travel routes that takes into account multiple constraints. This model is based on a collaborative filtering recommendation algorithm and addresses the issue of disorganized travel route recommendations. To improve upon the k-mean clustering algorithm, the proposed model introduces the Dynamic Gaussian Kernel Density K-means algorithm. After the data was processed, the initial clustering center was determined and k-means clustering was performed. Subsequently, the travel route recommendation model was created by integrating various constraints. The study’s proposed algorithm was compared with alternative algorithms, and the experimental results demonstrated superior performance across a range of datasets, with the minimum sum of squared errors and a running time of approximately 1.4 seconds - a noteworthy improvement. Comparative experiments were conducted on various forgetting coefficients in the model, and the forgetting coefficient with the lowest sum-of-squares of errors was selected to replace the existing one. Upon comparing the proposed research model with other models, it was found that the former had greater accuracy and recall, amounting to 98.1% and 96.8% respectively. This suggests that the proposed research model serves as a more efficient solution for travel route recommendation.

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Section: Trr Modelmentioning

confidence: 99%

Multiple Constraint Hybrid Travel Route Recommendation Model Based on Collaborative Filtering Recommendation Algorithm

Zhang

2024

SCPE

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“…At present, the education of advanced mathematics is still dominated by "cramming" education. The teacher is the education center in the classroom, and the students passively learn and teach knowledge [12]. Most higher vocational colleges are established by upgrading, merging or restructuring of technical secondary schools, adult colleges, and employee colleges.…”

Section: 1outdated Education Content and Single Education Systemmentioning

confidence: 99%

Integration and optimization of higher mathematics teaching resources based on collaborative filtering algorithm

Zhao

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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As a basic subject in cuniversities, advanced mathematics education has become more and more important, and with the rapid improvement of message technique, the application of message technique in advanced mathematics education has become a general trend. The traditional higher mathematics education methods are outdated, the education efficiency is low, and the graphics display effect is not ideal. With the improvement of science and technique and the advent of the modern electrical age, education methods need to advance with the times. It is particularly important to explore a education method that can organically combine the traditional mathematics education mode with modern message technique, unify the practicability and feasibility of education and education, which can not only facilitate teachers' education but also help students to improve their learning effects. This paper is based on the collaborative filtering algorithm. In the personalized recommendation system, more core is the recommendation algorithm. Collaborative filtering is the most familiar and widely used recommendation algorithm.

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Weight prediction and recognition of latent subject terms based on the fusion of explicit & implicit information about keyword

Li,

Jiang,

Jiang

et al. 2023

Engineering Applications of Artificial Intelligence

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Collaborative Filtering Algorithm based on Data Mixing and Filtering

Cited by 8 publications

References 6 publications

Multiple Constraint Hybrid Travel Route Recommendation Model Based on Collaborative Filtering Recommendation Algorithm

Multiple Constraint Hybrid Travel Route Recommendation Model Based on Collaborative Filtering Recommendation Algorithm

Integration and optimization of higher mathematics teaching resources based on collaborative filtering algorithm

Weight prediction and recognition of latent subject terms based on the fusion of explicit & implicit information about keyword

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