Recommender Systems with Machine Learning - Fundamentals of Recommender Systems

The video tutorial covers the taxonomy of recommender systems, item context, and user rating matrix. It discusses the quality of recommender systems and evaluation techniques. The tutorial explains content-based and collaborative filtering, including user-based, item-based, and model-based approaches. It introduces machine learning-based recommender systems using Python, focusing on data preparation and insight collection. The course concludes with a discussion on projects.

What is the primary focus of the first section of the video?

• Machine learning algorithms

• Taxonomy and evaluation of recommender systems

• Data visualization methods

• Project management techniques

Which of the following is NOT a type of collaborative filtering model discussed?

• Item-based

• User-based

• Content-based

• Model-based

What programming language is used for machine learning-based recommender systems in the video?

• JavaScript

• Java

• Python

• C++

What technique is used in content-based recommender systems to analyze text data?

• Decision Trees

• K-means clustering

• Principal Component Analysis

• TF-IDF

Which algorithm is mentioned for use in collaborative filtering recommender systems?

• Support Vector Machine

• K-nearest neighbor

• Linear Regression

• Random Forest