Building a content-based recommendation system with the tf-idf algorithm - Part 1

Beginning today, we are commencing a series of articles on constructing a recommendation system. Our objective is to implement and code the theoretical concepts that underlie it.

Everyone is accustomed to encountering recommended products on e-commerce platforms or online news websites. Amazon, perhaps, was the first retailer to effectively implement the surge fostered by recommendations and, in this series of articles, our objective is to explore in depth how such a system is practically constructed. We will discover that there are in fact various approaches and particularly focus on content-based recommendations. Even within this constrained scenario, we will find numerous possibilities for customization and utilize the widely adopted tf-idf algorithm. Finally, we will apply these concepts to an open dataset, recommending articles for the Huffington Post.

• Building a content-based recommendation system with the tf-idf algorithm - Part 2
• Building a content-based recommendation system with the tf-idf algorithm - Part 3
• Building a content-based recommendation system with the tf-idf algorithm - Part 4

The following authoritative textbook on this topic merits consultation. This book extends beyond recommendation systems and covers a myriad of expansive and general data mining topics. It notably emphasizes implementations for managing vast quantities of data.

Mining of Massive Datasets (Leskovec, Rajaraman, Ullman)

Without further ado and as usual, let's begin with a few prerequisites to correctly understand the underlying concepts. Continue here.