A Survey of Personalization Techniques for Recommender Systems

This survey provides a broad overview of personalization techniques used in recommender systems. It covers various methods, including content-based filtering, collaborative filtering, and hybrid approaches, explaining how these techniques leverage user data and preferences to provide tailored recommendations. The survey discusses the strengths and weaknesses of different approaches and identifies key challenges and future research directions in the field of personalized recommendation.