A Survey of Collaborative Filtering Techniques

Collaborative filtering is a widely used technique for making personalized recommendations. It predicts a user's preferences based on the preferences of similar users or items. This survey provides a comprehensive overview of various collaborative filtering algorithms, including memory-based and model-based approaches, and discusses their strengths and weaknesses in different application scenarios. The paper explains how these algorithms work and provides a foundation for understanding personalization systems like Dynamic Yield.