Building Recommender Systems using Photon ML

author: Bee-Chung Chen, LinkedIn Corporation
author: Alex Shelkovnykov, LinkedIn Corporation
author: Josh Fleming, LinkedIn Corporation
author: Xianxing Zhang, LinkedIn Corporation
author: Paul Ogilvie, LinkedIn Corporation
author: Deepak Agarwal, LinkedIn Corporation
published: Sept. 16, 2016,   recorded: August 2016,   views: 3699

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 59:58
Watch Part 2
Part 2 51:40


Recommendation systems have become ubiquitous for web applications. Given significant heterogeneity in user preference, 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 first 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 (LinkedIn’s open-sourced machine learning library) to train recommendation models and serve the results to users. Participants will get hands-on experience in training models of different levels of granularity to improve model performance and 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.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: