MTS: Tell us about your role at Taboola and how you arrived at this position?
Gil Chamiel: I am Director of Data Science and Algorithm Engineering at Taboola. This means that I am in charge of our algorithmic development efforts and research. Before I came to Taboola, I completed my PhD at the University of New South Wales in Sydney, Australia where I focused on AI research for web personalization.
I joined Taboola at the end of 2010 when it was still a very small company of around 20 people worldwide – today we are nearly 800. I started as an algorithm engineer as part of a team that consisted of software developers and a few algorithm engineers. As we grew, we decided to dedicate more resources towards algorithmic efforts and established an algorithm engineering team that I then led. The team grew and became a group consisting of several teams of data scientists/machine learning researchers and software engineers. In the past 18 months or so, we have been heavily investing in our Deep Learning knowledge and capabilities. This included studying new modeling techniques as well as the engineering of production-level scalable deep learning pipelines. The result is a strategic shift in Taboola’s R&D. We graduated most of our algorithmic stacks to be based on deep learning and adopted it as the default technique.
MTS: How does Taboola’s predictive recommendation engine work?
Gil: The challenge is matching content, products, apps, videos, etc (out of hundreds of thousands of possible recommendations at any given time) to users in specific contexts in a way that increases both short/long term engagement and revenue. We look at over 100 factors including previous engagement with Taboola recommendations and at the user’s browsing behavior outside of Taboola.
We then build predictive models that try to estimate the probability that users will engage with a certain item in the future. For example, imagine predicting when an article is being written what’s the…