The following month, KDnuggets editors collectively answered the questions in the subsequent article 21 Must-Know Data Science Interview Questions and Answers. Looking to utilize ChatGPT in new and exciting ways -- to both learn more about ChatGPT itself, and learn about data science interview question topics -- we decided to resurrect those same questions on the septennial anniversary of the original, and pose them to ChatGPT. I will preface this article with the clear statement that all of the answers to the questions in this article have been provided by ChatGPT. Do with that information what you will. I would encourage readers to compare these answers with those provided by the KDnuggets editors in 2016, in order to see which answers are more thorough, which are more accurate, and which just read better.

A recommendation system is a type of information filtering system. By drawing from huge data sets, the system's algorithm can pinpoint accurate user preferences. Once you know what your users like, you can recommend them new, relevant content. Netflix, YouTube, Google, Amazon etc are all examples of recommendation systems in use. The systems provide users with relevant suggestions based on the choices they make.

In this project, you will learn how similarity based collaborative filtering recommendation systems work, how you can collect data for building such systems. You will learn what are some different ways you to compute similarity between users and recommend items based on products interacted by other similar users. You will learn to create user item interactions matrix from the original dataset and also how to recommend items to a new user who does not have any historical interactions with the items. Note: This course works best for learners who are based in the North America region.

I recently gave a talk about recommender systems at the Data Science Sydney meetup (the slides are available here). This post roughly follows the outline of the talk, expanding on some of the key points in non-slide form (i.e., complete sentences and paragraphs!). The first few sections give a broad overview of the field and the common recommendation paradigms, while the final part is dedicated to debunking five common myths about recommender systems. The key reason why many people seem to care about recommender systems is money. For companies such as Amazon, Netflix, and Spotify, recommender systems drive significant engagement and revenue. But this is the more cynical view of things.