Recommender Systems with Machine Learning - KNN Implementation-2

The video tutorial guides viewers through the process of developing a KNN model. It begins with setting up the model using cosine metric and brood algorithm, followed by fitting the model with a movie user matrix. The tutorial then explains how to select a random query index and calculate distances and indices using KNN. Finally, it identifies a movie ID and demonstrates how to recommend neighbors based on this ID.

What is the significance of the 'distances' and 'indices' in the KNN model?

What ID is chosen as the query index in the KNN model?

What is the next step after obtaining the movie ID in the KNN model?