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 first step in developing a KNN model?

• Choosing the number of neighbors

• Defining the model and setting metrics

• Collecting data

• Evaluating the model

Which metric is used in the KNN model setup?

• Euclidean

• Manhattan

• Hamming

• Cosine

What is the purpose of fitting the KNN model?

• To train the model with data

• To evaluate the model

• To test the model

• To visualize the data

How is a random query index generated in the KNN process?

• Through a random choice from the matrix shape

• Using a fixed index

• By using the last index

• By selecting the first index

What is calculated after generating a random query index?

• Feature scaling

• Distances and indices

• Model accuracy

• Data normalization

What is the next step after identifying the movie ID from the query index?

• Calculating model accuracy

• Recommending five neighbors

• Collecting more data

• Visualizing the data

What is the movie ID identified from the query index in the video?

• 176

• 1302

• 2021

• 1500