Recommender Systems with Machine Learning - Item-Based Collaborative Filtering

The video tutorial covers item-based collaborative filtering, starting with an introduction to collaborative filtering types. It details the steps for data preparation, including merging datasets and using libraries like pandas and numpy. The tutorial explains how to gain data insights using matplotlib and implement K Nearest Neighbors (KNN) for item-based filtering. It guides viewers through building a recommendation engine, testing it, and using random sampling for book recommendations. The session concludes with a summary and instructions to start with Jupyter Notebook.

What is the main focus of item-based collaborative filtering?

Describe the steps involved in preparing data for item-based collaborative filtering.

What role does K nearest neighbors play in item-based collaborative filtering?

Explain how a recommendation engine is built using item-based collaborative filtering.

How is randomness incorporated in selecting books for recommendations?