Maroua Benleulmi‡, Ibtissem Gasmi‡, Nabiha Azizi§, Nilanjan Dey|Corresponding author: Maroua Benleulmi ( m.benleulmi@univ-eltarf.dz ) © Maroua Benleulmi, Ibtissem Gasmi, Nabiha Azizi, Nilanjan Dey. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. Citation:
Benleulmi M, Gasmi I, Azizi N, Dey N (2025) Explainable AI and deep learning models for recommender systems: State of the art and challenges. JUCS - Journal of Universal Computer Science 31(4): 383-421. https://doi.org/10.3897/jucs.122380 |  |
Recommender systems have a pivotal function in delivering customized and pertinent suggestions to clients on the basis of their preferences and activities. The present paper presents a thorough overview of deep learning-based recommender systems, explores their application to enhance performance, and overcomes limitations. The survey encompasses fundamental models of recommender systems; moreover, it also delves into key deep learning models.
This discussion focuses on the effective integration of deep learning techniques into recommender systems. Real-world applications highlight the effectiveness of these approaches in capturing complex and nonlinear patterns from large-scale data.
This paper concludes by reflecting on challenges encountered in this research field and outlines potential future directions, offering valuable insights for academics and professionals in the field of recommender systems based on deep learning.