Recommender Systems with Machine Learning - Section Overview

This video tutorial covers various aspects of recommender systems, starting with an overview of the module. It delves into the taxonomy of recommender systems, item context, and user rating matrix. The tutorial also discusses the quality of recommender systems and evaluation techniques, both online and offline. Filtering techniques, including content-based and collaborative filtering, are explored. The video concludes with discussions on error matrix and overfitting, providing a comprehensive understanding of recommender systems.

Describe the evaluation techniques used for assessing the quality of recommender systems.

How can data sets be partitioned to improve recommendation systems?