October 26, 2018
The Potential for Personalization in Web Search
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.
Susab Dumais is a technical fellow and managing deputy director of MSR AI. Her research is at the intersection of information retrieval and human-computer interaction. She is interested in algorithms and interfaces for improved information retrieval, as well as general issues in human-computer interaction.
She has been at Microsoft Research since July 1997. Her current research focuses on gaze-enhanced interaction, the temporal dynamics of information systems, user modeling and personalization, novel interfaces for interactive retrieval, and search evaluation. Previous research studied a variety of information access and management challenges, including personal information management, desktop search, question answering, text categorization, collaborative filtering, interfaces for improving search and navigation, and user/task modeling. She has worked closely with several Microsoft groups (Bing, Windows Desktop Search, SharePoint Portal Server and Office Online Help) on search-related innovations.
Prior to coming to Microsoft, Susan co-developed a statistical method for concept-based retrieval known as Latent Semantic Indexing. You can find pointers to this work on the Bellcore (now Telcordia) LSI page.