Towards AI-Aided Decision Making: Preference Learning and Recommendations from Multimodal Data
Hady W. Lauw is an Associate Professor at the School of Information Systems, Singapore Management University (SMU), where he directs the computer science programme and leads the Preferred.AI research group working on artificial intelligence and machine learning research, with a focus on modeling preferences and recommender systems. His research is funded by a prestigious NRF Fellowship of the Singapore National Research Foundation. Formerly, he served as postdoctoral researcher at Microsoft Research in Silicon Valley, as well as scientist at A*STAR’s Institute for Infocomm Research. Earlier, he received his PhD from Nanyang Technological University. He is currently serving as the Chair of the Singapore Chapter of ACM SIGKDD. For more information, see http://www.hadylauw.com.
With pervasive digitization, two trends are emerging. One is the proliferation of choices in our consumption of both physical and digital goods and services.
The other is the “datafication” of our behaviors, whereby users’ preferences increasingly manifest through multiple modalities of preference signals, e.g., consumptions, ratings, reviews, networks, images.
These trends point to the opportunity for AI-aided decision making that leverages machine learning to customize user experiences.
In this talk, we provide an overview of our research in leveraging multimodal preference signals towards realizing several stages of a recommendation framework, including preference elicitation and modeling, as well as recommendation delivery.
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