Building Recommender Systems using Photon ML
August 31, 2016
On Aug. 15, 2016, the Machine Learning Algorithm team at LinkedIn gave a hands-on tutorial on how to build recommender systems using our recently open-sourced machine learning library Photon ML at the KDD conference. We were excited to see a large crowd that could not fit into the room gathered to learn this topic and the strong interest in the data mining community in tools and practical guidance on building recommender systems.
Recommendation systems have become ubiquitous for web applications. Because each member's preferences are very different, providing personalized recommendations is key to the success of such systems. To achieve this goal at scale, using machine-learned models to estimate user preference from user feedback data is essential. Providing an easy-to-use and flexible machine learning library for practitioners to build personalization models is the key to productivity, agility, and developer happiness.
In this tutorial, we give an overview of the components required for building an end-to-end web recommender system and then focus on how to use Photon ML to train recommendation models and serve the results to users. The tutorial gives you hands-on experience in training models of different levels of granularity to improve model performance and allows you to perform the “modeling loop” consisting of training a model, scoring candidate items using the model, seeing recommended items in a web UI, giving feedback to a number of recommended items, and then training a model again using the newly generated feedback.
Here is the outline of the tutorial:
- Introduction to recommender systems
- Overview of different components needed to build a web-based recommender system end-to-end
- Overview of models and algorithms provided by the Photon ML library
Hands-on exercise in different recommendation models trained using Photon ML and deployed to a simple web UI
- Logistic regression model
- Generalized linear mixed effect model
- Interactive modeling with your feedback/ratings given to the recommended items
To take the tutorial, visit the Photon ML Tutorial page on GitHub. To learn more about methods for building recommender systems, see Statistical Methods for Building Recommender Systems.