Design a recommendation system for an e-commerce platform that suggests products to users based on their browsing history and purchase behavior. The recommendation system should utilize collaborative filtering to generate personalized recommendations for each user. Requirements: 1. Implement collaborative filtering to analyze user-item interactions (browsing history, purchases). 2. Use a similarity metric (e.g., cosine similarity, Pearson correlation) to measure the similarity between users or items. 3. Provide recommendations for each user based on their similarity with other users or items. 4. Evaluate the performance of the recommendation system using appropriate metrics (e.g., precision, recall, F1-score).

Design a recommendation system for an e-commerce platform that suggests products to users based on their browsing history and purchase behavior. The recommendation system should utilize collaborative filtering to generate personalized recommendations for each user. Requirements: 1. Implement collaborative filtering to analyze user-item interactions (browsing history, purchases). 2. Use a similarity metric (e.g., cosine similarity, Pearson correlation) to measure the similarity between users or items. 3. Provide recommendations for each user based on their similarity with other users or items. 4. Evaluate the performance of the recommendation system using appropriate metrics (e.g., precision, recall, F1-score).