Traditionally search engines returned the same results to everyone who asks the same question.\u00a0 However, using a single ranking for everyone in every context limits how well a search engine can do in providing relevant information.\u00a0 In this talk I outline a framework to quantify the “potential for personalization\u201d 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.\u00a0 Finally I will conclude by highlighting important challenges in developing personalized systems at Web scale including system optimization, evaluation, transparency and serendipity.<\/p>\n","protected":false},"excerpt":{"rendered":"
Traditionally search engines returned the same results to everyone who asks the same question.\u00a0 However, using a single ranking for everyone in every context limits how well a search engine can do in providing relevant information.\u00a0 In this talk I outline a framework to quantify the “potential for personalization\u201d which we use to characterize the 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