C2M2 invites you to join us in welcoming Anton Bezuglov, Vericast as a part of our C2M2 Distinguished Speaker Series.
Tuning Personalized Recommendations with the Multi-Armed Bandit Approach
While content-based and collaborative filtering are well-known types of product recommendation engines, their real-life application is sometimes problematic. This is due to the cold start issues, data veracity, or the multitude of hyperparameters. A combination of these factors makes it difficult to forecast which approach will produce the best fit products and for how long it will remain the best. This talk focuses on the optimization scheme where multiple algorithms run simultaneously and compete for the user audience. This optimization is more flexible when compared to A/B or A/B/n testing and it saves more user conversions.
Anton Bezuglov has a Ph.D. in Computer Science and Engineering from the University of South Carolina. He worked in academia for 12 years — first as a Professor of Computer Science (Benedict College), then as a Professor of Data Analytics (Buena Vista University). Since 2020, he has been a Data Scientist at Vericast. His areas of focus are digital advertising, ad optimization, brand safety, and personalized recommendations.