Recommender Systems Complete Course Beginner to Advanced - Project 2: Movie Recommendation System Using Collaborative Fi

The video tutorial guides viewers through the process of developing a KNN model. It begins with setting up the model using a cosine metric and brute algorithm. The tutorial then demonstrates how to fit the model with a movie user matrix. A random query index is selected, and distances and indices are calculated using the KNN model. The tutorial identifies the movie ID from the query index and sets up recommendations for the identified movie ID, preparing for further exploration in the next video.

What is the first step in developing the KNN model?

• Evaluating the model

• Setting up the model and defining metrics

• Defining the number of neighbors

• Loading the dataset

Which metric is used in the KNN model setup?

• Euclidean

• Manhattan

• Cosine

• Hamming

What is the purpose of fitting the KNN model?

• To train the model with the dataset

• To visualize the data

• To test the model's accuracy

• To evaluate the model

How is a random query index generated?

• Through a random choice from the matrix shape

• By selecting the first index

• Using a fixed number

• By using the last index

What is calculated after generating the query index?

• The number of neighbors

• The model's accuracy

• Distances and indices

• The dataset size

What is identified using the query index?

• The number of neighbors

• The model's accuracy

• The movie ID

• The dataset size

What is the next step after identifying the movie ID?

• Recommending five neighbors

• Evaluating the model

• Visualizing the data

• Testing the model's accuracy