At BuzzFeed we generate hundreds of articles a day, so choosing better headlines can save us from substantial losses in our audience engagement. Our solution is a tool that takes in multiple headline and thumbnail options for an article and decides which combination is most effective. In this talk, I discuss the models that perform best for this tool under different product scenarios. I also discuss causal analysis of the effectiveness of this tool when A/B testing is infeasible.
Full-stack data scientist and manager keen on solving emerging machine learning problems. Completed graduate work in machine learning with research on information diffusion. Interests include social networks, NLP, and deep learning.
Data Council, PO Box 2087, Wilson, WY 83014, USA - Phone: +1 (415) 800-4938 - EIN: 46-3540315 - Email: community (at) datacouncil.ai