Interacting with search systems, such as Web search engines, is the primary means of information access for most people. Search providers have invested billions of dollars developing search technologies, which power search engines and feature in many of today's virtual assistants (including Google Assistant, Amazon Alexa, Microsoft Cortana, and others). For decades, search has offered a plentiful selection of research challenges for computer scientists and the advertising models that fund industry investments are highly lucrative. Given the phenomenal success, search is often considered a "solved problem." There is some truth to this for fact-finding and navigational searches, but the interaction model and the underlying algorithms are still brittle in the face of complex tasks and other challenges, for example, presenting results in non-visual settings such as smart speakers.15 As a community, we need to invest in evolving search interaction to, among other things, address a broader range of requests, embrace new technologies, and support the often under-served "last mile" in search interaction: task completion.
The retrieval and comprehension of information is important in many settings. Billions of search queries reach search engines daily and searching skills are now even taught in schools. Search interaction has been studied by information science, information retrieval (IR), and human-computer interaction (HCI) researchers. Information scientists have examined the cognitive and behavioral mechanisms in the search process. IR researchers have developed new methods to collect and find information, including, recently, increased use of machine learning. HCI researchers have studied interactions with technology to develop interfaces to support activities such as information finding and sensemaking. Future opportunities are plentiful, including the three areas discussed in this Viewpoint:
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