Wednesday, February 2, 2022
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You Don’t Know Me: The Need for Personalized Recommendation in Conversational AI Assistants
Abstract: In this talk I will begin by discussing the need for personalized recommendation in conversational AI assistants and the fundamental challenges that make this problem difficult — both in general and in existing conversational AI architectures. I will next discuss some initial progress my research group has made in addressing these challenges as well as our attempt to understand the broader scope of possible interactions in natural, context-driven conversational recommendation. I will conclude with a summary of potential directions forward in the near-term as well as longer-term challenges.
Scott Sanner is an Associate Professor in Industrial Engineering, Cross-appointed in Computer Science, and a faculty affiliate of the Vector Institute. Previously Scott was an Assistant Professor at Oregon State University and before that he was a Principal Researcher at National ICT Australia (NICTA) and Adjunct Faculty at the Australian National University. Scott earned a PhD in Computer Science from the University of Toronto (2008), an MS in Computer Science from Stanford University (2002), and a double BS in Computer Science and Electrical and Computer Engineering from Carnegie Mellon University (1999).
Scott’s research spans a broad range of topics from the data-driven fields of Machine Learning and Information Retrieval to the decision-driven fields of Artificial Intelligence and Operations Research. Scott has applied the analytic and algorithmic tools from these fields to diverse application areas such as conversational recommender systems, adaptive user interfaces, and Smart Cities applications including predictive health analytics, transport optimization, power systems security, and residential HVAC control.
Scott has served as Program Co-chair for the 26th International Conference on Automated Planning and Scheduling (ICAPS) and is currently an Associate Editor for the Artificial Intelligence Journal (AIJ), the Journal of Artificial Intelligence Research (JAIR), and the Machine Learning Journal (MLJ). Scott was a co-recipient of paper awards from the AI Journal (2014), Transport Research Board (2016), and CPAIOR (2018) and a recipient of a Google Faculty Research Award (2020).
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