Roy Sasson is Outbrain’s Chief Data Scientist. His team applies predictive modeling and machine learning research that optimize Outbrain's recommendation system, in strategic areas such as user engagement, exploration effectiveness, document classification and mechanism design. Roy has a PhD in Applied Econometrics from Tel-Aviv University, where he has been teaching Econometrics since 2008. His academic research focused on gaining insights and predictions from data concerning the behavior of individuals, firms and even NBA coaches.
Presentations by Roy Sasson:
DevconTLV March 2016 Conference, Tuesday, March 22, 2016, 13:20We analyze the importance of two types of engagement signals in the domain of digital publishing: (1) content popularity, measured by page-views & click-through rates within the Outbrain network, and (2) content share-ability, measured by the proportion of Facebook shares. First, we show that counter-intuitively, the correlation between these signals is very low. Second, we characterize content which is very popular, but not shared, as “socially unacceptable” and show various examples. Third, we compare the predictive value and relative importance of each of these signals in production, as part of our recommendation system’s modeling.The results of the research are based on more than 100,000 articles, spanning across 200 publishers worldwide, which accounted for more than 1.8 Billion visits during two different time periods (Feb. 2015 and Nov. 2015). The implications of this study are important for data scientists, publishers and content marketers.(Joint work with Ram Meshulam, Outbrain)