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 first step in item-based collaborative filtering?

• Using random sampling for reference items

• Data preparation and merging datasets

• Implementing K-nearest neighbors

• Testing the recommendation engine

Which libraries are primarily used for data preparation in item-based collaborative filtering?

• PyTorch and OpenCV

• NumPy and Pandas

• Scikit-learn and Seaborn

• TensorFlow and Keras

In the context of item-based collaborative filtering, what is the purpose of K-nearest neighbors?

• To calculate distances between items

• To visualize data insights

• To merge multiple datasets

• To randomly select reference items

What is the final step in building a recommendation engine?

• Testing the recommendation system

• Implementing K-nearest neighbors

• Merging datasets

• Data preparation

How are reference items selected for recommendations in the discussed method?

• By user preference

• Using a fixed list of items

• Through a random sampling process

• Based on previous recommendations