CIIR Talk Series: Data, Methods, and Evaluation for Knowledge-Grounded Conversational Recommendation System

Content

Speaker 

Julian McAuley, UC San Diego

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.

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