Communications of the ACM

Vviewpoints

I M A G E B Y Z Y A B I C H

DOI: 10.1145/3195180

data sources (queries, clicks), but richer data (browse, cursor, physiology, spatial context, and so forth) is emerging that enables search systems to more fully represent interests and intentions, unlocking sophisticated modeling methods such as deep learning.

˲ Support for search interaction has focused on helping searchers build queries and select results. Search systems must evolve to support more complex search activities, leveraging technological advances to meet people’s growing expectations about search capabilities.

˲ Virtual assistants offer an alternative means to engage with search systems. Assistants support rudimentary question answering but will soon more fully comprehend question semantics, understand intent through dialog, and support task completion through skill chaining and skill recommendation.

INTERACTING WITH SEARCH sys- tems, such as Web search en- gines, 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 nonvisual 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 underserved “last mile” in search interaction: task completion.

Search Interaction

The retrieval and comprehension of
information is important in many set-
tings. 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. Infor-
mation scientists have examined the
cognitive and behavioral mechanisms
in the search process. IR researchers
have developed new methods to col-
lect and find information, including,
recently, increased use of machine
learning. HCI researchers have studied
interactions with technology to devel-
op interfaces to support activities such
as information finding and sensemak-
ing. Future opportunities are plentiful,
including the three areas discussed in
this Viewpoint:

˲ For more than a decade, search interaction has been immersed in a data revolution, using big (population) data1 and small (personal) data3 to model search activity and improve search experiences. This has used traditional