Recommender Systems with Machine Learning - KNN Implementation-1

The video tutorial covers the process of creating a CSR matrix from user ratings data, removing duplicates, and pivoting the data. It then explains how to build a KNN model using the cosine similarity metric and brute force algorithm. Finally, it demonstrates how to query the model with a random sample to find similar items and discusses the results.

What is the first step in preparing the data for analysis in this tutorial?

• Importing the CSR matrix from scipy

• Creating a pivot table

• Filling missing values with zero

• Developing a KNN model

Which function is used to remove duplicate entries from the dataset?

• drop_duplicates

• remove_duplicates

• delete_duplicates

• clear_duplicates

What is the purpose of filling missing values with zero in the pivot table?

• To improve data accuracy

• To prepare data for CSR matrix conversion

• To enhance visualization

• To reduce data size

Which metric is used in the KNN model for measuring similarity?

• Euclidean

• Manhattan

• Cosine

• Jaccard

What algorithm is used in the KNN model setup in this tutorial?

• Random Forest

• Brute Force

• Ball Tree

• KD Tree

How is a random query selected for processing in the KNN model?

• Using np.random.choice

• Using a predefined list

• Using a fixed index

• Using a loop

What is the final output of the query processing in this tutorial?

• A list of all neighbors

• The indices of all neighbors

• The nearest neighbor's name

• The distance to all neighbors