Recommender Systems with Machine Learning - Project Introduction-2

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

What type of recommender system is being developed in this course?

• Hybrid filtering

• Item-based collaborative filtering

• Content-based filtering

• User-based collaborative filtering

Which algorithm is used for the movie recommendation system discussed?

• Random Forest

• Decision Tree

• K nearest neighbors

• Support Vector Machine

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

• Data visualization

• Data preparation

• Algorithm optimization

• Model evaluation

Which programming language is used for building the recommendation engine?

• Java

• C++

• Python

• R

What software is used to implement the recommender system?

• PyCharm

• Eclipse

• Jupyter Notebook

• Visual Studio Code