Overview: In this talk we will discuss the modeling techniques behind personalized recommendation technology on the web. Examples of Recommender Systems range from simple statistical approaches like Amazon’s “people who bought X also bought Y” links, to complex AI-based approaches that drive feed ranking on sites like Facebook. We’ll discuss the models that drive these systems, look at the research questions that drive the future of this field in the coming years, and discuss their ethical implications. Guest Speaker: Julian McAuley, Ph.D., Assistant Professor, Computer Science and Engineering, UC San Diego Dr. McAuley’s research focuses on the linguistic, temporal, and social dimensions of opinions and behavior in social networks and other online communities. This includes understanding the facets of people’s opinions, the processes by which people “acquire tastes” for gourmet foods and beers, or even the visual dimensions that make clothing items compatible. He is perhaps best known for analyzing massive volumes of user data from online social communities including Amazon, Yelp, Reddit, Facebook, and BeerAdvocate. Before joining the Department of Computer Science and Engineering at UC San Diego, McAuley was a postdoctoral researcher at Stanford University from 2011 to 2014. He earned a Ph.D. from the Australian National University in 2011.