Dean’s Lecture Series

Wednesday, November 3, 2021
5:00-6:15 pm EST (Virtual)
Zoom link will be provided in the confirmation email upon registration for the event.

Bio:

Susan Dumais a Technical Fellow at Microsoft, Director of the Microsoft Research Labs in New England, New York City and Montreal, and an adjunct professor in the Information School at the University of Washington.  Prior to joining Microsoft, she was at Bell Labs where she developed Latent Semantic Analysis, an early word embedding technique for search.  Her current research focuses on personalization, email search, and large-scale behavioral log analysis.  She has worked with several Microsoft product groups (Bing, Windows Search, SharePoint, and Office Help) on search-related innovations, and holds several patents on novel retrieval algorithms and interfaces.  Susan has published widely in the fields of information retrieval, human-computer interaction, and cognitive science.   She is an ACM Fellow, was elected to the CHI Academy, the National Academy of Engineering (NAE) and the American Academy of Arts and Sciences (AAAS), and received the SIGIR Gerard Salton Award for lifetime achievement in information retrieval, the ACM Athena Lecturer Award, the Tony Kent Strix Award for outstanding contributions to IR, the ACM SIGCHI Research Award for lifetime achievement in HCI, and the Lifetime Achievement Award from Indiana University Department of Psychological and Brain Science.

Title:  The Potential for Personalization in Search

Abstract:  Traditionally Web search engines returned the same results to everyone who asks the same question.  However, using a single ranking for everyone in every context at every point in time limits how well a search engine can do in providing relevant information.  In this talk I present a framework to quantify the "potential for personalization” which is used to characterize the extent to which different people have different intents for the same query.  I describe several examples of how different types of contextual features are represented and used to improve search quality for individuals and groups.  Finally, I conclude by highlighting important challenges in developing personalized systems at Web scale including privacy, transparency, serendipity, and evaluation.