Personalized search

Traditionally search engines returned the same results to everyone who asks the same question. However, using a single ranking for everyone in every context limits how well a search engine can do in providing relevant information. In this talk I outline a framework to quantify the “potential for personalization” which we use to characterize the extent to which different people have different intents for a query. I will describe several examples of how we represent and use different kinds of contextual features to improve search quality for individuals. Finally I will conclude by highlighting important challenges in developing personalized systems at Web scale including system optimization, transparency, serendipity and evaluation.