Online Display Advertising Optimization with H2O

Online Display Advertising Optimization with H2O

By February 23, 2017Engineering

by Hassan Namarvar, Principal Data Scientist

We held a data science meetup (as a series of SF data mining meetups) at ShareThis headquarter at Palo Alto on Dec 9th, 2014. I presented our teamwork about online display advertising optimization. In online display advertising, the ultimate goal is to provide a best and relevant ad to an online user so that to influence him/her to take an action such as purchasing a product or signing up for a service. This requires estimating the probability of conversion for a given user, content, advertiser, location, device and so on. Conversion estimation is extremely challenging task since conversion events are rare events and data dimension is huge.  In this talk, I presented how, at ShareThis, we tackled conversion estimation problem. More specifically, I described how we built CPA models by leveraging ShareThis social media and Ad exchange datasets and applying the state-of-the-art machine learning algorithms such as GLM, GBM, and Random Forest provided by the H2O platform. I presented some results from live advertising campaigns at production to show the effectiveness of our approach. For more details, you can watch the following video and deck from yesterday talk

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ShareThis

ShareThis has unlocked the power of global digital behavior by synthesizing social share, interest, and intent data since 2007. Powered by consumer behavior on over three million global domains, ShareThis observes real-time actions from real people on real digital destinations.

About Us

ShareThis has unlocked the power of global digital behavior by synthesizing social share, interest, and intent data since 2007. Powered by consumer behavior on over three million global domains, ShareThis observes real-time actions from real people on real digital destinations.