User Behavior Prediction with Deep Learning: An Evaluation of CNN, LSTM, RNN, and Hybrid Models
subject: convolutional neural network (CNN); deep learning; hybrid CNN-LSTM model; long short-term memory (LSTM); recurrent neural network (RNN); user behavior prediction
description: 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. 2025 IEEE.
creator: Sreedevi, S.; Rajashekar; Ayesha, Samreen; Basha, Md Shaik Amzad; Prathibha, L.
source: 3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833150845-6;
language: English
type: Conference paper
coverage: Sreedevi S., Sri Venkateswara College of Engineering, Department of Management Studies, Tirupati, India; Rajashekar, Rajeev Gandhi Memorial College of Engineering and Technology, Department of Management studies, Nandyal, India; Ayesha S., Christ University, Department of Professional Studies, Bangalore, India; Basha M.S.A., Gitam School of Business, Gitam (Deemed to be University), Hyderabad, India; Prathibha L., Ashoka Women Engineering College (Autonomous), Kurnool, India