Millions of customers visited the Verizon Website and stores monthly to shop, from plans to products and accessories. Numerous task-specific recommender systems have been built at Verizon to enhance customer experience. Motivated by the latest development in deep learning, multi-task learning architectures can better understand customer behavior therefore greatly improve model performance, and also save time to learn new tasks. However, it's challenging to build such a system to: 1) digest heterogeneous data from purchases, online clicks, store visits to customer services and many more; and 2) accomplish various tasks where some of them might contradict with each other; and 3) efficiently train and tune large DL models at scale . At Verizon, we greatly benefited from the Ray ecosystem and built a novel multi-task learning recommender system, namely Pathways Recommender system (PaRS), which directly handles multiple abstract forms of data and is able to generalize across multiple recommendation tasks at Verizon. Last but not least, we explored built-in bias mitigation in PaRS to promote diverse, relevant and fair recommendations.
Luyang Wang is a lead distinguished scientist at Verizon AI Center. He lead a cross functional team focusing on developing end-to-end search and recommender services.
Xuning (Mike) Tang stands up and leads Verizon's Responsible AI practice which helps the company to engender trust and scale AI with confidence. Before joining Verizon, Mike was the leader of Berkeley Research Group (BRG)'s Artificial Intelligence & Machine Learning practice, where he initiated BRG's ethical AI market offering. Prior to that, he worked for Deloitte and Fannie Mae. Mike earned his Ph.D. from Drexel University in the College of Computing and Informatics. He has filed multiple patents and inventions and published more than 40 peer-reviewed research papers in top computer science journals and international conferences. He also serves as an associate editor and reviewer for multiple flagship journals in Artificial Intelligence and Machine Learning.
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