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

This video tutorial covers the development of an item-based collaborative filtering recommender system using the K nearest neighbours algorithm. It outlines the steps involved, including data preparation, gaining data insights, implementing the algorithm, and building and testing the recommendation engine. The tutorial uses Python and Jupyter Notebook as tools.

What type of filtering is being developed in the course?

• Hybrid filtering

• Item-based collaborative filtering

• Content-based filtering

• User-based collaborative filtering

Which algorithm is used for the movie recommendation system?

• Random Forest

• Support Vector Machine

• K Nearest Neighbors

• Decision Tree

What is the first step in implementing the K nearest neighbors algorithm?

• Algorithm optimization

• Model evaluation

• Data preparation

• Data visualization

Which programming language is used for building the recommendation engine?

• C++

• Java

• R

• Python

What software is used to implement the project?

• Jupyter Notebook

• Eclipse

• Visual Studio Code

• PyCharm