DR. MORRA, JONATHAN
Topic : SOLVING IMPOSSIBLE PROBLEMS BY COLLABORATING WITH AN AI
Abstract: At ZEFR we know that when when advertising on YouTube having an ad creative be around contextually relevant videos are incredibly important to the success of an ad. Because of this we cluster YouTube in such a way that advertisers can easily find videos that fit well With both their brand and their creative’s specific style.
To use our clients we use two different clustering strategies, a top down supervised learning approach and a bottom up unsupervised learning approach. The top down approach used using human annotated data and an active learning to rank system that find and classifies relevant videos while still creating A priority queue for reviewers to help train the algorithm. This approach utilizes a pointwise learning to rank algorithm that classifies videos.
Our clients are also interested in trending topics on YouTube. To serve this need we use unsupervised clustering on trending videos to surface clusters that are temporally relevant. This type of clustering allows ZEFR to highlight what users are currently interested in. We show how using Latent Dirichlet Allocation can help to solve this problem. Along the way we will show some of the tricks that produce an accurate unsupervised learning system.
Bio: Jon Morra is the Vice President of Data Science at ZEFR. In this role, he leads a team of data scientists responsible for creating data-driven models. Jon and his team are focused on bringing ZEFR’s wealth of information about video on the internet To help better drive customer’s needs and meet goal demands. Previously, Jon was the Director of Data Science at eHarmony, where he helped grow the data science team to support multiple business facets.
Jon holds a BS from Johns Hopkins and a Ph.D. from UCLA both in Biomedical Engineering.