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 recommended 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
Amit is the Senior Data Scientist at Teachers Pay Teachers, an online marketplace for teachers to buy, sell and share original educational resources.
Additionally, he teaches in the Data Analytics department at CUNY School of Professional Studies and has his PhD in Physics. Prior to TpT and a 2 year stint in advertising, Amit worked in various banks and hedge funds as quant for 12 years.
In his spare time, Amit is usually hiking, biking or skiing with his 2 daughters.