Content-based recommendation systems depend on item qualities and user profiles rather than social relationships to provide tailored content. The introduction defines recommendation paradigms and emphasizes content-based systems’ utility when user data is sparse. The literature review summarizes the evolution of content-based filtering from 1990s origins leveraging keywords to today's advanced feature extraction and user modeling approaches enhanced by machine learning. Comparative benchmarking elucidates performance trade-offs for different algorithms. Public datasets spanning diverse domains are analyzed to facilitate rigorous evaluation. Current research directions are highlighted including contextual modeling, knowledge infusion, conversational systems, and focus on diversity and human-centric metrics beyond accuracy.