Intelligent information recommendation algorithm under background of big data land cultivation

Tang, Haoxiang; Yang, Wenzhu; Zheng, Susheng

doi:10.1016/j.micpro.2020.103728

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…For example, from the perspective of user travel, Chen, C. et al proposed a path-planning algorithm that combines user clustering and improved genetic and rectangular region path-planning algorithms for better designing user-personalized travel paths [19]. Tang, H. et al proposed an intelligent information recommendation algorithm based on user preference mining in order to ameliorate the problem of information flooding in the era of big data [20]. Zhang, W. based on the promotion of the human-computer interaction trend for content generation, proposed an entrepreneurial learning platform based on human-computer interaction in order to utilize the innovation of intelligent systems and information growth to achieve the cultivation of students' innovation ability [21].…”

Section: Introductionmentioning

confidence: 99%

Innovative and entrepreneurial education reform strategy based on algorithmic recommendation of social media information sharing characteristics in colleges and universities

Dai

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, the student documents are processed by experimental strategy, with the topic of college innovation and entrepreneurship resources recommendation, word diffusion and improved LDA topic model distribution training, set the training topic model and the number of topic top-words, and compare the high-frequency vocabulary and the probability distribution error of the 80% user coverage case and 90% user coverage case. Explore the stability of social media information pushing, the performance comparison results of both combine LDA based on user comment information text and LDA modeling directly using user comment information text as a reference. Select the experimental objects of innovation and entrepreneurship resources in colleges and universities, obtain the recommendation information of innovation and entrepreneurship resources in social media, and analyze the accuracy and satisfaction of algorithmic recommendation. Analyze the sharing characteristics of algorithmic recommendation information on social media and propose educational countermeasures against its adverse effects. According to the analysis, the improved LDA topic model results in a 15% and 85% increase in the number of same words in each topic when top_words is 10. When top_words is 20, the proportion of the same words in each theme is 15% and 78%, respectively. This indicates that the innovation and entrepreneurship education topic features are stable, and the accuracy and satisfaction of the algorithmic recommendation have improved.

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Section: Introductionmentioning

confidence: 99%

Innovative and entrepreneurial education reform strategy based on algorithmic recommendation of social media information sharing characteristics in colleges and universities

Dai

2023

Applied Mathematics and Nonlinear Sciences

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“…Different K values will affect the number of data in each category and the location of the center point, thus affecting the clustering results. The other is that the randomly selected initial clustering center will cause a large difference in clustering results [3].…”

Section: Introductionmentioning

confidence: 99%

Analysis of e-commerce users' preference behavior based on k-means algorithm

Xin

Zhang²

2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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In the process of continuous development of e-commerce, to better meet the needs of users and tap the consumption potential of users, personalized recommendation systems have emerged on various e-commerce platforms. Although the clustering algorithm is suitable for solving the user segmentation problem, the traditional K-means algorithm has some shortcomings, such as the quality of the initial center point determined randomly is not high, and there is no definite criterion to select the value of K. Therefore, this paper proposes an optimized K-means algorithm, which uses the value of effectiveness index CH to determine the value of K, and combines with particle swarm algorithm to solve the initial center point. Experiments show that the optimization algorithm CH-PSO-K-means algorithm proposed in this paper has improved the DB index, accuracy rate, and error square index. The comprehensive performance of the clustering effect has been significantly improved, which can effectively solve the problem of e-commerce user segmentation with large data and multiple characteristics. The optimization algorithm not only makes up for the shortcomings of the K-means algorithm, but also improves the maintenance strategy of individual e-commerce users, which is conducive to enterprise cost control and profit improvement.

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Design of Personalized Catering Recommendation System based on Spark Platform

Liu

2022

2022 2nd Asia Conference on Information Engineering (ACIE)

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Intelligent information recommendation algorithm under background of big data land cultivation

Cited by 4 publications

References 19 publications

Innovative and entrepreneurial education reform strategy based on algorithmic recommendation of social media information sharing characteristics in colleges and universities

Innovative and entrepreneurial education reform strategy based on algorithmic recommendation of social media information sharing characteristics in colleges and universities

Analysis of e-commerce users' preference behavior based on k-means algorithm

Design of Personalized Catering Recommendation System based on Spark Platform

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