Text Selections as Implicit Relevance Feedback

Users’ search activity has been used as implicit feedback to model search interests and improve the performance of search systems. In search engines, this behavior usually takes the form of queries and result clicks. However, richer data on how people engage with search results can now be captured at scale, creating new opportunities to enhance search. In this poster we focus on one type of newly-observable behavior: text selection events on search-result captions. We show that we can use text selections as implicit feedback to significantly improve search result relevance.