  As the demand for high-quality video content continues to rise, adaptive
video streaming plays a pivotal role in delivering an optimal viewing
experience. However, traditional content recommendation systems face challenges
in dynamically adapting to users' preferences, content features, and contextual
information. This review paper explores the integration of fuzzy logic into
content recommendation systems for adaptive video streaming. Fuzzy logic, known
for handling uncertainty and imprecision, provides a promising framework for
modeling and accommodating the dynamic nature of user preferences and
contextual factors. The paper discusses the evolution of adaptive video
streaming, reviews traditional content recommendation algorithms, and
introduces fuzzy logic as a solution to enhance the adaptability of these
systems. Through a comprehensive exploration of case studies and applications,
the effectiveness of fuzzy logic in improving user satisfaction and system
performance is highlighted. The review also addresses challenges associated
with the integration of fuzzy logic and suggests future research directions to
further advance this approach. The proposed framework offers insights into a
dynamic and context-aware content recommendation system, contributing to the
evolution of adaptive video streaming technologies.
