Search from Personal to Social Context: Progress and Challenges.

User and behavioral modeling plays a critical role in a variety of online services such as web search, advertising, ecommerce, and news recommendation. For example, our ability to accurately interpret the intent of a web search can be informed by knowledge of the web pages a searcher was viewing when initiating the search or recent actions of the searcher such as queries issued, results clicked, and pages viewed. In this talk, I will describe a recent framework for personalized search which improves the quality of search results by enabling a representation of a broad variety of context including the searcher’s long-term interests, recent activity, current focus, and other user characteristics. Then, I will review a variety of related work that extends these approaches from signals focused on the individual to social signals such as likes, cohorts, and affiliation networks. Finally, I’ll speculate on how social signals and networks can provide directions for relatively unexplored directions in social personalized retrieval.