<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the improvement of the quality of human life, various industries utilize deep learning technology to meet the needs of users. In this paper, after exploring the deep temporal model and deep forest algorithm (DF) model in extracting the characteristics of user behavior features, we propose a user behavior prediction model based on CNN-LSTM and add a front embedding layer as well as a feature fusion layer to improve it and increase the prediction accuracy. The weighted average method is used to integrate CNN-LSTM with DF for algorithmic model fusion, aiming to improve the robustness and stability of the model and achieve accurate predictions of user behavior. In the empirical analysis, the correct rate of the fusion model proposed in this paper exceeds the proper rate of several other models by 11.75-15.73%, and it can reach 11.2% recall at k=20. Meanwhile, the lower the user behavior level is, the higher the prediction accuracy of the CNNLSTM+DF algorithm is, which can reach up to 95.20%. The lower the average relative error and average absolute error are, which can reach 13.45% and 3.92min respectively, which verifies the validity of the fusion model proposed in this paper, and provides a reference for the research in the related fields.</p>
</abstract>

With the improvement of the quality of human life, various industries utilize deep learning technology to meet the needs of users. In this paper, after exploring the deep temporal model and deep forest algorithm (DF) model in extracting the characteristics of user behavior features, we propose a user behavior prediction model based on CNN-LSTM and add a front embedding layer as well as a feature fusion layer to improve it and increase the prediction accuracy. The weighted average method is used to integrate CNN-LSTM with DF for algorithmic model fusion, aiming to improve the robustness and stability of the model and achieve accurate predictions of user behavior. In the empirical analysis, the correct rate of the fusion model proposed in this paper exceeds the proper rate of several other models by 11.75-15.73%, and it can reach 11.2% recall at k=20. Meanwhile, the lower the user behavior level is, the higher the prediction accuracy of the CNNLSTM+DF algorithm is, which can reach up to 95.20%. The lower the average relative error and average absolute error are, which can reach 13.45% and 3.92min respectively, which verifies the validity of the fusion model proposed in this paper, and provides a reference for the research in the related fields.

Big Data User Behaviour Prediction Model Incorporating Deep Learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Big Data User Behaviour Prediction Model Incorporating Deep Learning"}

With the improvement of the quality of human life, various industries utilize deep learning technology to meet the needs of users. In this paper, after...

Big Data User Behaviour Prediction Model Incorporating Deep Learning

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 24 lut 2024

Przyjęty: 24 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1450

Słowa kluczowe
Deep learning, DF, CNN-LSTM, CNN-LSTM+DF, User behavior prediction

© 2024 Xiang Huang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Big Data User Behaviour Prediction Model Incorporating Deep Learning

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 24 lut 2024

Przyjęty: 24 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1450

Słowa kluczoweDeep learning, DF, CNN-LSTM, CNN-LSTM+DF, User behavior prediction

© 2024 Xiang Huang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Deep learning, DF, CNN-LSTM, CNN-LSTM+DF, User behavior prediction