Information Retrieval and Search Behavior

Information retrieval research examines how systems find and rank documents in response to user queries, and how people actually behave when they search — what they type, what they click, and when they give up. Getting this right matters because even small improvements in ranking quality, measured across billions of daily queries, translate into meaningful differences in whether people find accurate, useful information or settle for something good enough. A central challenge is learning from implicit signals like clickthrough data, which reflects user interest but is noisy and shaped by the very rankings it is meant to evaluate. Ongoing work focuses on building language models that better capture query intent, designing fairer and more transparent ranking algorithms, and understanding how generative AI is beginning to reshape the relationship between search systems and the people who use them.

Works

39,779

Total citations

391,541

Keywords

Information RetrievalSearch EnginesUser BehaviorLearning to RankQuery AnalysisRelevance Feedback