Implementing a User-Based Recommendation System from Scratch in Python

1. 1Build a user-item interaction matrix using Python
2. 2Calculate similarity between users using a distance metric such as Jaccard or Pearson
3. 3Find nearest neighbors for each user
4. 4Generate recommendations for a target user based on nearest neighbors' preferences
5. 5Test and evaluate the performance of the recommendation system using metrics like precision and recall

Data scientists and machine learning engineers can benefit from this tutorial to improve recommendation systems in their products

💡 User-based collaborative filtering recommends items to a user based on similarities with other users

Build a user-based recommendation system from scratch in Python #recommendationsystem #collaborativefiltering