A prediction model of massive 5G network users’ revisit behavior based on telecom big data
Users in 5G networks will generate a large amount of access data, which makes it difficult to accurately predict users’ revisit behavior.Therefore, a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteri...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-02-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023026/ |
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| author | Yudi SUN |
| author_facet | Yudi SUN |
| author_sort | Yudi SUN |
| collection | DOAJ |
| description | Users in 5G networks will generate a large amount of access data, which makes it difficult to accurately predict users’ revisit behavior.Therefore, a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteristic data was extracted from the telecom big data to build a data set.Multi order weighted Markov chain model was introduced.The model weight value was obtained by calculating the autocorrelation coefficient of each order, and the statistics of the model were calculated.After analysis, the one-step transition probability matrix of Markov chain with each step size was obtained, so as to accurately predict the revisit behavior of massive users in 5G network.The experimental results show that the proposed model has the lowest mean error and standard deviation, as well as the highest accuracy, recall, precision and F1 indicators, which can prove that the proposed method has a very obvious advantage in predicting users’ revisit behavior. |
| format | Article |
| id | doaj-art-31a2c4b0dc1743cba0c1c96b0ddbd1a2 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-02-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-31a2c4b0dc1743cba0c1c96b0ddbd1a22025-01-15T02:59:07ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-02-013915716259570846A prediction model of massive 5G network users’ revisit behavior based on telecom big dataYudi SUNUsers in 5G networks will generate a large amount of access data, which makes it difficult to accurately predict users’ revisit behavior.Therefore, a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteristic data was extracted from the telecom big data to build a data set.Multi order weighted Markov chain model was introduced.The model weight value was obtained by calculating the autocorrelation coefficient of each order, and the statistics of the model were calculated.After analysis, the one-step transition probability matrix of Markov chain with each step size was obtained, so as to accurately predict the revisit behavior of massive users in 5G network.The experimental results show that the proposed model has the lowest mean error and standard deviation, as well as the highest accuracy, recall, precision and F1 indicators, which can prove that the proposed method has a very obvious advantage in predicting users’ revisit behavior.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023026/telecom big dataprediction of users’ revisit behaviormulti order weighted Markov chain modelone step transition probability matrixautocorrelation coefficient |
| spellingShingle | Yudi SUN A prediction model of massive 5G network users’ revisit behavior based on telecom big data Dianxin kexue telecom big data prediction of users’ revisit behavior multi order weighted Markov chain model one step transition probability matrix autocorrelation coefficient |
| title | A prediction model of massive 5G network users’ revisit behavior based on telecom big data |
| title_full | A prediction model of massive 5G network users’ revisit behavior based on telecom big data |
| title_fullStr | A prediction model of massive 5G network users’ revisit behavior based on telecom big data |
| title_full_unstemmed | A prediction model of massive 5G network users’ revisit behavior based on telecom big data |
| title_short | A prediction model of massive 5G network users’ revisit behavior based on telecom big data |
| title_sort | prediction model of massive 5g network users revisit behavior based on telecom big data |
| topic | telecom big data prediction of users’ revisit behavior multi order weighted Markov chain model one step transition probability matrix autocorrelation coefficient |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023026/ |
| work_keys_str_mv | AT yudisun apredictionmodelofmassive5gnetworkusersrevisitbehaviorbasedontelecombigdata AT yudisun predictionmodelofmassive5gnetworkusersrevisitbehaviorbasedontelecombigdata |