A Survey of Collaborative Filtering Techniques

Collaborative filtering is a widely used technique for making personalized recommendations. It works by identifying users with similar preferences and recommending items that those users have liked in the past. This survey paper provides a comprehensive overview of different collaborative filtering algorithms, including memory-based and model-based approaches, and discusses their strengths and weaknesses. It lays a foundational understanding of how personalized experiences can be delivered by leveraging user interaction data.