Recommender Systems with Machine Learning - Online Evaluation Techniques

The video tutorial discusses evaluation techniques for recommender systems, focusing on online and offline methods. Online evaluation includes direct user feedback, AB testing, and controlled experimentation, each with its own challenges and benefits. Crowdsourcing is also explored as a method to gather user input. The tutorial concludes with a brief introduction to offline evaluation techniques.

What is the main issue with controlled experimentation in evaluation techniques?

How does crowdsourcing contribute to evaluation techniques?

What is the significance of using various evaluation techniques for improving recommendation systems?