  This review explores machine unlearning (MUL) in recommendation systems,
addressing adaptability, personalization, privacy, and bias challenges. Unlike
traditional models, MUL dynamically adjusts system knowledge based on shifts in
user preferences and ethical considerations. The paper critically examines
MUL's basics, real-world applications, and challenges like algorithmic
transparency. It sifts through literature, offering insights into how MUL could
transform recommendations, discussing user trust, and suggesting paths for
future research in responsible and user-focused artificial intelligence (AI).
The document guides researchers through challenges involving the trade-off
between personalization and privacy, encouraging contributions to meet
practical demands for targeted data removal. Emphasizing MUL's role in secure
and adaptive machine learning, the paper proposes ways to push its boundaries.
The novelty of this paper lies in its exploration of the limitations of the
methods, which highlights exciting prospects for advancing the field.
