Online Display Advertising Optimization with H2O @ ShareThis

Speaker: Hassan Namarvar, Data Scientist at ShareThis

Food/Drinks: Sandwich Wraps and Beer/Soda will be provided by ShareThis

Topic: In online display advertising, the ultimate goal is to provide the best and most relevant ad to an online user 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 and advertiser. Conversion estimation is extremely challenging task since conversion events are rare and data dimensionality is huge.

In this presentation, I will describe how we tackle conversion estimation problem at ShareThis. More specifically, I will address how we build CPA models by leveraging ShareThis social media and Ad Exchange datasets and applying state-of-the-art machine learning algorithms such as GLM, GBM, Random Forest and Deep Learning provided by the H2O platform. I will provide some results from real advertising campaigns to show the effectiveness of our approach.

Speaker Bio: Hassan is a Principal Data Scientist at ShareThis Inc. He works on online advertising optimization by modeling real time bidding transactional and user behavior datasets using large-scale machine learning techniques. Before joining to ShareThis, Hassan was a Principal Data Scientist at Shopzilla Inc (now Connexity) where he significantly improved Shopzilla comparison shopping search engine relevancy and revenue. Prior to that, Hassan was a Data Mining engineer at Amazon.com where he focused on web analytics, development of parallel processing framework, graph analysis, recommendation systems, NLP, IR and automated website performance monitoring system. Hassan holds a Ph.D. in Biomedical Engineering from University of Southern California. At USC, he developed large-scale dynamic synapse neural networks and their applications in speech recognition. He has published more than 10 papers in journals and international conferences.