Speaker: Julian McAuley, UC San Diego
Talk Title: Data, methods, and evaluation for knowledge-grounded conversational recommendation systems
Date: Friday, September 20, 2024 - 1:30 - 2:30 PM EDT (North American Eastern Daylight Saving Time)
Zoom Access: Zoom Link and reach out to Hamed Zamani or Dan Parker for the passcode.
Abstract: In this talk we'll explore the current landscape of conversational recommendation in light of new developments on Large Language Models. We'll look at ways that current models can potentially be improved by exploring new datasets, methods, and evaluation protocols for conversational recommendation.
Bio: Julian McAuley has been a Professor at UC San Diego since 2014, where he works on applications of machine learning to problems involving personalization, and teaches classes on personalized recommendation. Broadly speaking, his lab’s research seeks to develop machine learning techniques for settings where differences among individuals explain significant variability in outcomes. A core instance of this problem is that of recommender systems, one of the core areas of his lab’s research, where he develops technologies that underlie algorithms like those used for recommendations on Netflix, Amazon, or Facebook. He likes bicycling and baroque keyboard.