Recommender Systems: Collaborative Filtering and Content-Based Filtering
The article discusses recommender systems, focusing on Collaborative Filtering and Content-Based Filtering methods. Collaborative Filtering uses user interaction data, while Content-Based Filtering relies on item characteristics for personalized recommendations. Hybrid systems combine both methods for better results.
Collaborative Filtering and Content-Based Filtering are two fundamental approaches used in recommender systems to provide personalized recommendations to users. These methods are designed to address the challenges of information overload and help users discover relevant content or products.
COLLABORATIVE FILTERING:
Collaborative Filtering is based on the idea that users who have shown similar preferences in the past are likely to have similar preferences in the future. It relies on user-item interaction data, such as user ratings, reviews, or purchase history, to make recommendations. There are two main types of collaborative…
