Web search engines utilize behavioral signals to develop search experiences tailored to individual users. To be effective, such personalization relies on access to sufficient information about each user\u2019s interests and intentions. For new users or new queries, profile information may be sparse or non-existent. To handle these cases, and perhaps also improve personalization for those with profiles, search engines can employ signals from users who are similar along one or more dimensions, i.e., those in the same cohort. In this paper we describe a characterization and evaluation of the use of such cohort modeling to enhance search personalization. We experiment with three pre-defined cohorts\u2014topic, location, and top-level domain preference\u2014independently and in combination, and also evaluate methods to learn cohorts dynamically. We show via extensive experimentation with large-scale logs from a commercial search engine that leveraging cohort behavior can yield significant relevance gains when combined with a production search engine ranking algorithm that uses similar classes of personalization signal but at the individual searcher level. Additional experiments show that our gains can be extended when we dynamically learn cohorts and target easily-identifiable classes of ambiguous or unseen queries.<\/p>\n","protected":false},"excerpt":{"rendered":"
Web search engines utilize behavioral signals to develop search experiences tailored to individual users. To be effective, such personalization relies on access to sufficient information about each user\u2019s interests and intentions. For new users or new queries, profile information may be sparse or non-existent. 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