5 research outputs found
Characterizing Search Behavior in Productivity Software
Complex software applications expose hundreds of commands to users through intricate menu hierarchies. One of the most popular productivity software suites, Microsoft Office, has recently developed functionality that allows users to issue free-form text queries to a search system to quickly find commands they want to execute, retrieve help documentation or access web results in a unified interface. In this paper, we analyze millions of search sessions originating from within Microsoft Office applications, collected over one month of activity, in an effort to characterize search behavior in productivity software. Our research brings together previous efforts in analyzing command usage in large-scale applications and efforts in understanding search behavior in environments other than the web. Our findings show that users engage primarily in command search, and that re-accessing commands through search is a frequent behavior. Our work represents the first large-scale analysis of search over command spaces and is an important first step in understanding how search systems integrated with productivity software can be successfully developed
Conversations with Documents. An Exploration of Document-Centered Assistance
The role of conversational assistants has become more prevalent in helping
people increase their productivity. Document-centered assistance, for example
to help an individual quickly review a document, has seen less significant
progress, even though it has the potential to tremendously increase a user's
productivity. This type of document-centered assistance is the focus of this
paper. Our contributions are three-fold: (1) We first present a survey to
understand the space of document-centered assistance and the capabilities
people expect in this scenario. (2) We investigate the types of queries that
users will pose while seeking assistance with documents, and show that
document-centered questions form the majority of these queries. (3) We present
a set of initial machine learned models that show that (a) we can accurately
detect document-centered questions, and (b) we can build reasonably accurate
models for answering such questions. These positive results are encouraging,
and suggest that even greater results may be attained with continued study of
this interesting and novel problem space. Our findings have implications for
the design of intelligent systems to support task completion via natural
interactions with documents.Comment: Accepted as full paper at CHIIR 2020; 9 pages + Appendi