Interaction With Search Engines: Opportunities And Challenges

 

INTERACTING WITH SEARCH for most people, information is accessed primarily through systems such as Web search engines. Search companies have spent billions of dollars developing search technologies that power search engines and are used in many of today's virtual assistants (including Google Assistant, Amazon Alexa, Microsoft Cortana, and others). For decades, search has provided a plethora of research challenges for computer scientists, and the advertising models that fund industry investments are extremely profitable. Search is frequently regarded as a "solved problem" due to its phenomenal success.

This is true for fact-finding and navigational searches, but the interaction model and underlying algorithms remain brittle in the face of complex tasks and other challenges, such as presenting results in non-visual settings such as smart speakers. We must invest in evolving search interaction as a community in order 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

In many situations, information retrieval and comprehension are critical. Millions of search queries are sent to search engines every day, and searching skills are now taught in schools. Search interaction has been investigated.

researchers in information science, information retrieval (IR), and human-computer interaction (HCI). Information scientists have investigated the cognitive and behavioral mechanisms that occur during the search process. IR researchers have created new methods for gathering and locating information, including the recently increased use of machine learning. HCI researchers have studied human-technology interactions in order to create interfaces to support activities such as information retrieval and sensemaking. Future opportunities abound, including the three topics covered in this Viewpoint:

• For over a decade, search interaction has been undergoing a data revolution, with big (population) data1 and small (personal) data3 being used to model search activity and improve search experiences. Traditional data sources (queries, clicks) have been used, but richer data (browse, cursor, physiology, spatial context, and so on) is emerging, allowing search systems to more fully represent interests and intentions, umodelingsophisticated modeling methods such as deep learning.
• Support for search interaction has primarily focused on assisting searchers in creating queries and selecting results. To meet people's growing expectations about search capabilities, search systems must evolve to support more complex search activities, leveraging technological advances.
• Virtual assistants provide an alternative way to interact with search systems. Assistants currently support rudimentary question answering but will soon understand question semantics, intent through dialogue, and task completion through chaining and skill recommendation.

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Read Also:    Data Revolution

                         Evolving Capabilities and Expectations

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About Kanwal Jabeen