Open Access
| Issue |
ITM Web Conf.
Volume 86, 2026
5th International Conference on Current Research in Engineering and Technology (ICCRET-2026)
|
|
|---|---|---|
| Article Number | 01011 | |
| Number of page(s) | 15 | |
| Section | AI & Intelligent Computing | |
| DOI | https://doi.org/10.1051/itmconf/20268601011 | |
| Published online | 05 June 2026 | |
- J. Salminen, C. Kandpal, A. M. Kamel, S. G. Jung, B. J. Jansen, Creating and detecting fake reviews of online products. J. Retail. Consum. Serv. 64, 102771 (2022). https://doi.org/10.1016/j.jretconser.2021.102771 [Google Scholar]
- E. F. Cardoso, R. M. Silva, T. A. Almeida, Towards automatic filtering of fake reviews. Neurocomputing 309, 106–116 (2018). https://doi.org/10.1016/j.neucom.2018.04.074 [Google Scholar]
- J. Yao, Y. Zheng, H. Jiang, An ensemble model for fake online review detection based on data resampling, feature pruning, and parameter optimization. IEEE Access 9, 16914–16927 (2021). https://doi.org/10.1109/access.2021.3051174 [Google Scholar]
- R. Singhal, R. Kashef, A weighted stacking ensemble model with sampling for fake reviews detection. IEEE Trans. Comput. Soc. Syst. 11(2), 2578–2594 (2023). https://doi.org/10.1109/TCSS.2023.3268548 [Google Scholar]
- S. Dasgupta, J. Buckley, A multi-embedding convergence network on Siamese architecture for fake reviews. arXiv:2401.05995 (2024). https://arxiv.org/abs/2401.05995 [Google Scholar]
- J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, R. Shah, Signature verification using a “Siamese” time-delay neural network, in Advances in Neural Information Processing Systems, vol. 6 (MIT Press, Cambridge, 1993). https://doi.org/10.1142/S0218001493000339 [Google Scholar]
- R. Gupta, I. Kashyap, V. Jindal, SBiLM: Siamese Bi-LSTM model for handling imbalance in fake review detection, in Proceedings of the 5th International Conference on Innovative Data Communication Technologies and Application, Procedia Computer Science, vol. 235 (Elsevier, Amsterdam, 2024), pp. 1157–1166. https://doi.org/10.1016/j.procs.2024.04.110 [Google Scholar]
- K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, D. Brown, Text classification algorithms: a survey. Information 10(4), 150 (2019). https://doi.org/10.3390/info10040150 [Google Scholar]
- B. Saxena, S. Goyal, A. Kumari, A. Agarwal, Boosting the accuracy of fake review prediction using the synthetic minority oversampling technique, in Proceedings of the International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, November 18–19 (IEEE, New York, 2022), pp. 156–161. https://doi.org/10.1109/icccis56430.2022.10037717 [Google Scholar]
- N. Serrano, A. Bellogín, Siamese neural networks in recommendation. Neural Comput. Appl. 35(19), 13941–13953 (2023). https://doi.org/10.1007/s00521-023-08610-0 [Google Scholar]
- M. Ennaouri, A. Zellou, A scoring approach for detecting fake reviews using MRCS similarity metric enhanced by personalized k-means. Bull. Electr. Eng. Inform. 14(1), 587–595 (2025). https://doi.org/10.11591/eei.v14i1.8288 [Google Scholar]
- M. S. Javed, H. Majeed, H. Mujtaba, M. O. Beg, Fake reviews classification using a deep learning ensemble of shallow convolutions. J. Comput. Soc. Sci. 4(2), 883–902 (2021). https://doi.org/10.1007/s42001-021-00114-y [Google Scholar]
- F. Khurshid, Y. Zhu, J. Hu, M. Ahmad, M. Ahmad, Battering review spam through ensemble learning in imbalanced datasets. Comput. J. 65(7), 1666–1678 (2022). https://doi.org/10.1093/comjnl/bxab006 [Google Scholar]
- Z. Shunxiang, Z. Aoqiang, Z. Guangli, W. Zhongliang, L. KuanChing, Building fake review detection model based on sentiment intensity and PU learning. IEEE Trans. Neural Netw. Learn. Syst. 34(10), 6926–6939 (2023). https://doi.org/10.1109/tnnls.2023.3234427 [Google Scholar]
- P. Hajek, A. Barushka, M. Munk, Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Comput. Appl. 32(23), 17259–17274 (2020). https://doi.org/10.1007/s00521-020-04757-2 [Google Scholar]
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