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- W4387162014 abstract "With the rapid popularization and development of mobile communication technology, the number of users is increasing rapidly, and users' requirements for network service quality are getting higher and higher, and it has been difficult for the traditional methods to improve customer satisfaction quickly and effectively. Firstly, we conducted data pre-processing to analyze the magnitude of the correlation between each factor and different total satisfaction rating values through gray correlation analysis, so as to realize the quantitative performance of the degree of influence and get the main broad categories of factors affecting user satisfaction. In this paper, we establish a user satisfaction prediction model based on feedforward neural network. First, we performed data processing. For the rejection column aspect, we only keep the field columns that are common among the data sets, while using multiple interpolation for default value filling and replacing the non-numeric data in the table with numeric values. We train the training dataset with a feedforward neural network and choose gradient descent as the backward propagation algorithm part of the feedforward neural network. Finally, the prediction set is fed into the trained model to get the final answer." @default.
- W4387162014 created "2023-09-30" @default.
- W4387162014 creator A5089285601 @default.
- W4387162014 date "2023-08-18" @default.
- W4387162014 modified "2023-09-30" @default.
- W4387162014 title "User satisfaction prediction based on feed-forward neural network algorithm" @default.
- W4387162014 doi "https://doi.org/10.1109/icsece58870.2023.10263520" @default.
- W4387162014 hasPublicationYear "2023" @default.
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