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Evaluation Model of Click Rate of Electronic Commerce Advertising Based on Fuzzy Genetic Algorithm

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Abstract

In order to improve the ability of quantitative evaluation of e-commerce advertising click rate, a model of e-commerce advertising click rate evaluation based on fuzzy genetic algorithm is proposed. The big data information sampling model of e-commerce advertisement click rate evaluation, based on the mining result of e-commerce advertisement click rate evaluation information, carries on the adaptive mining and fusion clustering processing to the e-commerce advertisement click rate evaluation data, extracts the similarity information of the e-commerce advertisement click rate distribution set, carries on the e-commerce advertisement click rate adaptive evaluation according to the similarity contrast method, carries on the feature coding in the e-commerce advertisement click rate evaluation process through the fuzzy genetic optimization method, establishes the e-commerce advertisement click rate reliability feature distribution function, combines the statistical feature analysis and the fuzzy feature cluster analysis method to evaluate and predict the e-commerce advertisement click rate. The simulation results show that this method has better adaptability, higher accuracy and better convergence.

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Acknowledgements

The study was supported by “General Research Projects of Philosophy and Social Sciences in Colleges and Universities in Jiangsu Province, China (Grant No. 2021SJA1140)” and “Special Project for Doctoral Research of Xuzhou College of Industrial Technology, China (Grant No. XGY2021EA01)”.

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Authors and Affiliations

  1. School of Management, China University of Mining and Technology, Xuzhou, 221000, China

    Peisen Song, Chen Chen & Lei Zhang

  2. School of Information and Electrical Engineering, Xuzhou College of Industrial Technology, Xuzhou, 221000, China

    Peisen Song

  3. College of Business Administration, Xuzhou College of Industrial Technology, Xuzhou, 221000, China

    Chen Chen

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  1. Peisen Song
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  2. Chen Chen
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  3. Lei Zhang
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Correspondence to Peisen Song.

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Song, P., Chen, C. & Zhang, L. Evaluation Model of Click Rate of Electronic Commerce Advertising Based on Fuzzy Genetic Algorithm. Mobile Netw Appl 27, 936–945 (2022). https://doi.org/10.1007/s11036-022-01916-8

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