Today, we’re joined by Federico Bianchi, a Postdoctoral Researcher at Università Bocconi. He discusses testing recommender systems, the essential features for any platform with that purpose, testing the relevance of these systems, and how to handle the biases they generate. With the continuous growth of e-commerce and online media in recent years, there are an increasing number of software-as-a-service recommender systems (RSs) accessible today. Users can get new content from recommender systems, which range from news articles (Google News, Yahoo News) to series and flicks (Netflix, Disney+, Prime Videos), and even products (Amazon, eBay). Today, there are so many products and information available on the internet that no single viewer can possibly see everything that is offered. This is where recommendations come in, allowing products and information to be classified according to their expected relevance to the user's preferences. They compared offline recommendations to online evaluation platforms, which allow researchers to evaluate their systems in live, real-time scenarios with real people. Federico discusses the benefits of offline modeling and evaluates the speed and convenience of testing algorithms with predetermined datasets. However, because these statistics are not tied to actual users, there are a lot of biases to consider.