Keynote Speaker, Julian McAuley
"Fashion Recommender Systems: Some old tasks, some new tasks, and some emerging challenges"
Abstract: In this talk I'll give a high-level overview of the history and some of the emerging problems at the intersection of recommendation and fashion. We'll start by introducing some of the "traditional" approaches to fashion recommendation, and discuss the main challenges involved in handling complex visual data, dealing with temporal signals, collecting reliable ground-truth (etc.). Next we'll talk about some of the unique problems in fashion that don't fall into usual recommendation paradigms, such as wardrobe recommendation, size estimation (etc.). Finally, we'll discuss some of the emerging trends in this area, especially focusing on fairness and bias issues in fashion recommendation data.
Bio: Julian McAuley has been a professor in the Computer Science Department at the University of California, San Diego since 2014. Previously he was a postdoctoral scholar at Stanford University after receiving his PhD from the Australian National University in 2011. His research is concerned with developing predictive models of human behavior using large volumes of online activity data.