Human resource recommendation algorithm based on improved frequent itemset mining

Liu, Zhaoshan; Ma, Yiming; Zheng, Huihua; Liu, Dege; Liu, Jing

doi:10.1016/j.future.2021.08.017

Cited by 22 publications

(12 citation statements)

References 2 publications

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“…After analysis, combined with the research on the local area network faults and current fault diagnosis technology [18], it can be found that the local area network environment is relatively complex; if the traditional qualitative fault diagnosis method is used, it is often difficult to effectively model the problem and extract the expert knowledge of network fault. e rapid changes in scale and hierarchy, and the emergence of various business scenarios and military scenarios, the requirements for network management and fault diagnosis will only increase.…”

Section: Network Failure Scenario Analysismentioning

confidence: 99%

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

2022

Mobile Information Systems

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In order to meet the new requirements of fault diagnosis response and intelligent degree in the current computer network, a fault detection of computer communication network based on an improved neural network algorithm is proposed. First, from the perspective of deep learning, based on the KDD99 data set, the network fault diagnosis method based on the convolutional neural network model is studied, and the data conversion operation of grayscale matrixed raw data is proposed. And experiments are carried out, the convolutional neural network structure is designed according to the scale of data features, a series of optimization studies including discarding learning, gradient optimization algorithm, and data enhancement based on this is carried out, and the establishment of the entire fault diagnosis model is completed. The experimental results show that, in the diagnostic model designed in this paper, the Tanh activation function is used in the first fully connected layer to achieve the best convergence speed. During the training process, it can start to converge after about 24 iterations, and the accuracy rate of the model training process can reach 98.1%, verifying the correctness and superiority of the algorithm and model.

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Section: Network Failure Scenario Analysismentioning

confidence: 99%

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

2022

Mobile Information Systems

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“…Personalized Attraction Recommendation Algorithm Based on Bayesian Network Learning. It is a graph whose operations involve knowledge related to probability [20], often using probability as a way to predict the relationship between two.…”

Section: Experiments Evaluation Index Of Evaluationmentioning

confidence: 99%

Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

Wang

Qin

Tang

et al. 2022

Journal of Function Spaces

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With the deepening of tourists’ demand for tourism services, the personalization of online tourists has gradually become an application of personalized recommendation technology. According to the application requirements of personalized scenic spot recommendation, this paper uses social networks and Bayesian networks to fully mine the matching degree between users and scenic spots for personalized recommendation. Add social network factors to the recommendation of tourist attractions, and fully tap the social network relationship between users. First, the users are clustered by the coupling bidirectional clustering algorithm. Then, DBSCAN (density-based noise application spatial clustering) algorithm is used to cluster scenic spots. Finally, two stable user sets and scenic spot sets are applied to the personalized recommendation algorithm to predict the user’s next upcoming scenic spot. The algorithm is compared with some traditional algorithms in the dataset. The algorithm deals with the similarity of user attributes and user behavior and uses content-based algorithm to deal with the relationship between scenic spots; com datasets have better performance.

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“…e results of this study show that this method can significantly improve the job match and work efficiency of employees. Liu et al [23] found that individual differences and the recommendation process of employees will affect the management of human resources, and they proposed a data mining human resources recommendation algorithm to solve this problem. e positive matrix factorization (PMF) model is used to deal with implicit HR data and similarity data.…”

Section: Related Workmentioning

confidence: 99%

Human Resource Decision-Making and Recommendation Based on Hadoop Distributed Big Data Platform

Chen

2022

Mathematical Problems in Engineering

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The innovative development of an enterprise is a necessary factor for its long-term development, and science and technology is a necessary driving force for its development. An effective management of human resources management will find more high-quality talent, and it will also be efficient management of the enterprise staff. The efficiency of the traditional human resource management is relatively low; the approach will produce larger errors for the enterprise. This study combines the Hadoop distributed platform and big data technology to conduct a decision recommendation research on the relevant factors of human resource management. In this study, the decision tree is used to classify the data related to human resource management. The data feature of human resource will be predicted by the neural network method, and human resource managers will refer to the prediction data from the output layer of neural network. Human resource managers will make corresponding decisions and recommendations. The research results show that Hadoop technology and big data technology have better accuracy in human resource management recommendation system, which contains ConvLSTM method and decision tree method in this study. The maximum prediction error of the ConvLSTM algorithm in predicting the relevant factors of the human resource decision-making system is only 2.67%, and this part of the error comes from the employee-job matching index. The smallest forecast is only 1.98%.

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Human resource recommendation algorithm based on improved frequent itemset mining

Cited by 22 publications

References 2 publications

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

Human Resource Decision-Making and Recommendation Based on Hadoop Distributed Big Data Platform

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