Enhancing Arabic Content Recommendation Systems Using BERT-Based Semantic Representations and Hybrid Filtering Methods

Rasha Falah Kadhem

References:

1. A. A. Al-Ajlan et al., “Recommender system for Arabic content using sentiment analysis of user reviews,” Electronics, 2023. https://doi.org/10.3390/electronics12132785.
2. S. Al-Ghuribi et al., “A comparative study of sentiment-aware collaborative filtering algorithms for Arabic recommendation systems,” IEEE Access, vol. 12, pp. 174441–174454, 2024. https://doi.org/10.1109/ACCESS.2024.3489658.
3. C. Channarong et al., “HybridBERT4Rec: A hybrid (content-based filtering and collaborative filtering) recommender system based on BERT,” IEEE Access, 2022. https://doi.org/10.1109/access.2022.3177610.
4. R. Widayanti et al., “Improving recommender systems using hybrid techniques of collaborative filtering and content-based filtering,” Journal of Applied Data Sciences, 2023. https://doi.org/10.47738/jads.v4i3.115.
5. Y. Su et al., “Improvement of reading platforms assisted by the spring framework: A recommendation technique integrating the KGMRA algorithm and BERT model,” Heliyon, vol. 11, 2025. https://doi.org/10.1016/j.heliyon.2025.e42191.
6. N. Darraz et al., “Integrated sentiment analysis with BERT for enhanced hybrid recommendation systems,” Expert Systems with Applications, vol. 261, p. 125533, 2024. https://doi.org/10.1016/j.eswa.2024.125533.
7. P. D. Reddy et al., “Enhancing content based collaborative filtering recommendations using weighted word embeddings,” in Proc. 15th Int. Conf. Computing Communication and Networking Technologies (ICCCNT), 2024, pp. 1–7. https://doi.org/10.1109/ICCCNT61001.2024.10724582.
8. M. Srifi et al., “Evaluation of recent advances in recommender systems on Arabic content,” Journal of Big Data, vol. 8, 2021. https://doi.org/10.1186/s40537-021-00420-2.
9. M. Srifi et al., “A novel deep learning-based recommender system for Arabic content,” Journal of Intelligent & Fuzzy Systems, vol. 43, pp. 437–449, 2022. https://doi.org/10.3233/jifs-212274.
10. R. Morzelona et al., “Sentiment-aware content recommendation using LSTM-based collaborative filtering,” Research Journal of Computer Systems and Engineering, 2022. https://doi.org/10.52710/rjcse.53.
11. T. Wang et al., “Item-based collaborative filtering with BERT,” in Proc. 3rd Workshop on e-Commerce and NLP, 2020. https://doi.org/10.18653/v1/2020.ecnlp-1.8.
12. I. Karabila et al., “BERT-enhanced sentiment analysis for personalized e-commerce recommendations,” Multimedia Tools and Applications, vol. 83, pp. 56463–56488, 2023. https://doi.org/10.1007/s11042-023-17689-5.
13. P. Do et al., “Developing a BERT based triple classification model using knowledge graph embedding for question answering system,” Applied Intelligence, vol. 52, pp. 636–651, 2021. https://doi.org/10.1007/s10489-021-02460-w.
14. S. Sharma et al., “Automatic recommendation system based on hybrid filtering algorithm,” Education and Information Technologies, vol. 27, pp. 1523–1538, 2021. https://doi.org/10.1007/s10639-021-10643-8.
15. D. A. J. et al., “Advancing e-commerce recommendations: A comparative study of BERT-enhanced collaborative filtering and alternative approaches,” in Proc. Int. Conf. Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), 2024, pp. 1–6. https://doi.org/10.1109/ICAIQSA64000.2024.10882202.
16. D. Suhartono et al., “Book recommendation using double-stack BERT: Utilizing BERT to extract sentence relation feature for a content-based filtering system,” 2023, pp. 36–47. https://doi.org/10.1007/978-3-031-36402-0_3.
17. Z. Qiu et al., “U-BERT: Pre-training user representations for improved recommendation,” in Proc. AAAI, 2021, pp. 4320–4327. https://doi.org/10.1609/aaai.v35i5.16557.
18. R. Rwedhi et al., “Improving collaborative filter using BERT,” Journal of Kufa for Mathematics and Computer, 2023. https://doi.org/10.31642/jokmc/2018/100204.
19. M. J. Althobaiti et al., “BERT-based approach to Arabic hate speech and offensive language detection in Twitter: Exploiting emojis and sentiment analysis,” International Journal of Advanced Computer Science and Applications, 2022. https://doi.org/10.14569/ijacsa.2022.01305109.
20. B. N. M. Hoang et al., “Using BERT embedding to improve memory-based collaborative filtering recommender systems,” in Proc. RIVF Int. Conf. Computing and Communication Technologies, 2021, pp. 1–6. https://doi.org/10.1109/RIVF51545.2021.9642103.
21. R. H. El-Deeb et al., “Enhancing e-recruitment recommendations through text summarization techniques,” Information, 2025. https://doi.org/10.3390/info16040333.
22. S. Patil et al., “Enhancing movie recommendation systems with BERT: A deep learning approach,” in Proc. 5th Int. Conf. Emerging Technology (INCET), 2024, pp. 1–8. https://doi.org/10.1109/INCET61516.2024.10593284.
23. R. A. Machhout et al., “Enhanced BERT approach to score Arabic essay’s relevance to the prompt,” Communications of the IBIMA, 2024. https://doi.org/10.5171/2024.176992.
24. W. Nazih et al., “Arabic syntactic diacritics restoration using BERT models,” Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/3214255.
25. C. Channarong et al., “Hybrid (content-based filtering and collaborative filtering) recommender system based on BERT,” 2019. https://doi.org/10.58837/chula.sp.2019.43.
26. S. Saxena et al., “Transformers (BERT) based framework for web recommendations using sentiment-enriched web data,” Journal of Information Systems Engineering and Management, 2025. https://doi.org/10.52783/jisem.v10i11s.1632.
27. H. Chouikhi et al., “Arabic sentiment analysis using BERT model,” 2021, pp. 621–632. https://doi.org/10.1007/978-3-030-88113-9_50.
28. H. Lim et al., “A BERT-based multi-embedding fusion method using review text for recommendation,” Expert Systems, vol. 42, 2025. https://doi.org/10.1111/exsy.70041.
29. S. Khalifeh et al., “A book recommender system using collaborative filtering method,” in Int. Conf. Data Science, E-learning and Information Systems, 2021. https://doi.org/10.1145/3460620.3460744.