SUSAN DUMAIS, Microsoft
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.
Mechanical Engineering Building, Room 102