Using Graphs for High Quality Recommendations

03:30 PM - 04:25 PM on August 15, 2015, Room 701

amit bhattacharyya

Audience level:

intermediate

Watch:

http://youtu.be/A3p0wcq-Tag

Description

Using a graph data structure and community detection algorithms to provide recommendations to a cluster of similar buyers.

Abstract

Teachers Pay Teachers is an online marketplace for teachers to buy, sell and share original educational resources. As any marketplace grows, there is an increasing need to provide a customized experience so that the site feels like it is “for me”. We find that it is hard to extract good recommendations for a group using traditional recommender systems because of the focus on user-specific results.

By utilizing a graph data structure and community detection algorithms, we are able to make cluster-level recommendations so that teachers can find the most appropriate teaching resources based on their collective purchase history. In particular we find that this approach gives quality recommendations for users with very little purchase history, i.e. harnessing the power of “power users”. In this talk, we show how to use standard Python packages to identify clusters and make product recommendations.