In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users. Recommender systems are automated computer programs that match items to users in different contexts. Such systems are ubiquitous and have become an integral part of our daily lives. Examples include recommending products to users on a site like Amazon, recommending content to users visiting a website like Yahoo!, recommending movies to users on a site like Netflix, recommending jobs to users on LinkedIn, and so on. Given the significant heterogeneity in user preferences, providing personalized recommendations is key to the success of such systems.

Recommender systems are automated computer programs that match items to users in different contexts. Such systems are ubiquitous and have become an integral part of our daily lives. Examples include recommending products to users on a site like Amazon, recommending content to users visiting a website like Yahoo!, recommending movies to users on a site like Netflix, recommending jobs to users on LinkedIn, and so on. Given the significant heterogeneity in user preferences, providing personalized recommendations is key to the success of such systems. To achieve this goal at scale, using machine learning models to estimate user preference from feedback data is essential.