User Behavior Prediction with Deep Learning: An Evaluation of CNN, LSTM, RNN, and Hybrid Models

In this work, with a novel Hybrid CNN LSTM model, we present an in depth comparison and evaluation of various deep learning models (CNN, LSTM, RNN) on predicting user behaviour. To compare our proposed models we conducted detailed experimentation and rigorous performance comparisons using aforementioned metrics: accuracy, precision, recall, F1-score. The findings show that our RNN model performed incredibly well, reporting an accuracy of 99.86%. These results illustrate the power of the model in generalizing, and in capturing sequential dependencies in the user behaviour. On the other hand, the CNN model also performed very robustly, achieving 97.86% accuracy showing that it has powerful ability to extract spatial features. However, the LSTM model got decent results, with an accuracy of 87.14%. However, the Hybrid CNN-LSTM model's accuracy was for 80.29% which was up to the Hybrid CNN as mentioned above, and hence had lagged behind and has scope for improvement. Our proposed approach has provable advantages over existing methods, most notably, by using the RNN model outperforming the existing method. The RNN is able to detect sequential patterns more effectively than traditional techniques and show its ability to learn deeply to better predict user behaviour. Finally, we also hope that this investigation highlights two important directions for future RNN research: first, the promise of RNNs, and second, promising avenues for future research, such as refining hybrid architectures to yield improved performance.