A Survey of Personalization Techniques for Recommender Systems

Recommender systems use various methods to provide personalized suggestions to users. This paper reviews different personalization techniques, including content-based filtering (matching recommendations to user profiles), collaborative filtering (recommending items favored by similar users), and hybrid approaches that combine both. The survey offers insights into how these techniques enhance user experience and improve recommendation accuracy by tailoring content to individual preferences.