Overview of Recommender Algorithms – Part 3

This is the third in a multi-part post. In the first post, we introduced the main types of recommender algorithms by providing a cheatsheet for them. In the second post, covered the different types of collaborative filtering algorithms highlighting some of their nuances and how they differ from one another. In this blog post, we’ll… Read More Overview of Recommender Algorithms – Part 3

Overview of Recommender Algorithms – Part 2

This is the second in a multi-part post. In the first post, we introduced the main types of recommender algorithms by providing a cheatsheet for them. In this post, we’ll describe collaborative filtering algorithms in more detail and discuss their pros and cons in order to give a deeper understanding for how they work. Collaborative… Read More Overview of Recommender Algorithms – Part 2

Dithering

In this post we’ll look at a relatively low cost but high value technique for improving the quality of your recommendations, named dithering.  It’s a technique that re-orders a list of recommendations by slightly shuffling them around.  It is typically implemented in the recommendation post-processing component.  Despite its value, it doesn’t seem to have been… Read More Dithering

The Components of a Recommender System

A recommender system is made up of five core components (Figure 1). This post is intended to give the big picture. In future posts we’ll jump into details. Arguably, the core component is the one that generates recommendations for users; the recommender model (2).  It is responsible for taking data, such as user preferences and descriptions… Read More The Components of a Recommender System