Comparing Approaches for Query Autocompletion

Di Santo, G., McCreadie, R. , Macdonald, C. and Ounis, I. (2015) Comparing Approaches for Query Autocompletion. In: 38th Annual ACM SIGIR Conference (SIGIR 2015), Santiago, Chile, 9-13 Aug 2015, pp. 775-778. ISBN 9781450336215 (doi: 10.1145/2766462.2767829)

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Within a search engine, query auto-completion aims to predict the final query the user wants to enter as they type, with the aim of reducing query entry time and potentially preparing the search results in advance of query submission. There are a large number of approaches to automatically rank candidate queries for the purposes of auto-completion. However, no study exists that compares these approaches on a single dataset. Hence, in this paper, we present a comparison study between current approaches to rank candidate query completions for the user query as it is typed. Using a query-log and document corpus from a commercial medical search engine, we study the performance of 11 candidate query ranking approaches from the literature and analyze where they are effective. We show that the most effective approaches to query auto-completion are largely dependent on the number of characters that the user has typed so far, with the most effective approach differing for short and long prefixes. Moreover, we show that if personalized information is available about the searcher, this additional information can be used to more effectively rank query candidate completions, regardless of the prefix length.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Mccreadie, Dr Richard and Macdonald, Professor Craig and Ounis, Professor Iadh
Authors: Di Santo, G., McCreadie, R., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2015 The Authors
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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