Bayesian Deep Learning Based Exploration-Exploitation for Personalized Recommendations
Personalized Recommendation Systems offer a fundamental capability to identify the most appropriate content at the best time for the right individual. At Fidelity Investments, we consider personalized engagement across multiple channels as a natural extension of our deep client relationship. From a practical perspective, applications of recommender systems require an effective technique to balance exploration and exploitation. For that purpose, in this talk we will present a novel approach based on Bayesian Deep Learning. For exploitation, we show how to capture rich contextual information, and for exploration, we demonstrate how to quantify uncertainty stemming from machine learning models as well as the underlying data.
Xin Wang is a Senior Data Scientist at Fidelity Investments. He works on developing and applying AI and Machine Learning methods to enhance customer experience and engagement. Previously, he worked as a Research Scientist at Philips Research North America, developing Deep Learning based approaches for Automatic Annotation of Chest X-ray Images and Concept Mining in Echocardiogram reports. He holds a Ph.D. in Computer Science and Engineering from University of Connecticut advised by Professor Jinbo Bi, where his study focused on building Machine Learning algorithms and systems in Learning from multiple annotators, Multi-instance Learning and Multitask Feature Learning.