Computer Science
Revisiting recommender systems: an investigative survey
O. A. S. Ibrahim, E. M. G. Younis, et al.
This review maps the evolution of recommender systems—classifying collaborative, content-based, and hybrid approaches—while highlighting how machine learning and deep learning tackle cold-start, filter bubbles, and personalization. It also stresses fairness, transparency, and trust and points to future directions. Research conducted by Authors present in <Authors> tag.
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