In information retrieval, query auto completion (QAC), also known as type-ahead [Xiao et al., 2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the following functionality: given a preﬁx consisting of a number of characters entered into a search box, the user interface proposes alternative ways of extending the preﬁx to a full query. Ranking query completions is a challenging task due to the limited length of preﬁxes entered by users, the large volume of possible query completions matching a preﬁx, and the broad range of possible search intents. In recent years, a large number of query auto completion approaches have been proposed that produce ranked lists of alternative query completions by mining query logs. In this survey, we review work on query auto completion that has been published before 2016. We focus mainly on web search and provide a formal deﬁnition of the query auto completion problem. We describe two dominant families of approaches to the query auto completion problem, one based on heuristic models and the other based on learning to rank. We also identify dominant trends in published work on query auto completion, viz. the use of time-sensitive signals and the use of user-speciﬁc signals. We describe the datasets and metrics that are used to evaluate algorithms for query auto completion. We also devote a chapter to efﬁciency and a chapter to presentation and interaction aspects of query auto completion. We end by discussing related tasks as well as potential research directions to further the area.
['Fei Cai', 'Maarten de Rijke']
A Survey of Query Auto Completion in Information Retrieval