Ouahiba Remadnia
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ouahiba Remadnia has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Faiz Maazouzi
University of Souk Ahras
2 shared publications - Djalel Chefrour
Department of Computer Science, Mohamed-Cherif Messaadia University, Souk-Ahras, Algeria
1 shared publication - Raciel Yera
University of Jaén
1 shared publication - Rosa M. Rodríguez
University of Jaén
1 shared publication
Latest Publications
- 7 November 2025
- Published by Springer Nature
- p. 84-95
Post-hoc Recommendation Explanations in E-Learning: An Empirical Study Using Fuzzy Tools
- 7 November 2025
- Published by Springer Nature
- p. 84-95
Post-hoc Recommendation Explanations in E-Learning: An Empirical Study Using Fuzzy Tools
- Open Access
- 7 February 2025
- Published by Slovenian Association Informatika in Informatica
- Vol. 49 (8)
Hybrid Book Recommendation System Using Collaborative Filtering and Embedding Based Deep Learning
We propose a hybrid e-book recommendation mechanism that leverages collaborative filtering and contentbased recommendation paradigms to address inherent challenges in e-learning systems. For collaborative filtering, we present an innovative deep learning framework that utilizes embeddings to enhance accuracy and manage large datasets efficiently. This framework effectively addresses the cold start problem, thereby improving recommendation precision. In content-based recommendation, we introduce a regression-based technique to elevate system capabilities by incorporating content attributes. The integration of these techniques into our deep learning model creates a comprehensive and adaptable solution with scalability and effectiveness. Experiments on the Book Recommendation dataset demonstrate that our solution provides better suggestions and outperforms existing works in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), achieving values of 0.69 and 0.51, respectively
- 7 February 2025
- Published by Slovenian Association Informatika in Informatica
- Vol. 49 (8)
Open AccessHybrid Book Recommendation System Using Collaborative Filtering and Embedding Based Deep Learning
We propose a hybrid e-book recommendation mechanism that leverages collaborative filtering and contentbased recommendation paradigms to address inherent challenges in e-learning systems. For collaborative filtering, we present an innovative deep learning framework that utilizes embeddings to enhance accuracy and manage large datasets efficiently. This framework effectively addresses the cold start problem, thereby improving recommendation precision. In content-based recommendation, we introduce a regression-based technique to elevate system capabilities by incorporating content attributes. The integration of these techniques into our deep learning model creates a comprehensive and adaptable solution with scalability and effectiveness. Experiments on the Book Recommendation dataset demonstrate that our solution provides better suggestions and outperforms existing works in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), achieving values of 0.69 and 0.51, respectively