Large-scale Recommendation System In Production

Recommendation systems are critical to the success of today’s online commercial platforms.

On some of the largest commercial platforms, recommendations account for as much as 30% of the revenue. A 1% improvement in the quality of recommendations can translate into billions of dollars in revenue.

I’ve tried the more traditional content-based and collaborative filtering based recommendation systems. There are also models based on deep NN networks using RNN, CNN or the attention mechanisms. Large scale recommendation systems are not easy to build, and are often constrained by memory capacity. Most of the resources online about recommendation systems are about the models. I was curious about the system design of a large scale production system when I discovered the talk by Dr. Even Oldridge who leads the Nvidia’s recommendation engine team (Merlin).

Let’s take a look together:

How does a recommendation system work?

The following diagram displays a holistic view of the system’s components included in a recommendation system.

Recommender systems are trained using data gathered about the users, items, and their interactions, which include impressions, clicks, likes, mentions, and so on.

The front end displays the recommended results and is where the user interactions occur. These data are then logged and fed to the data lake for the regular process of model…