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Mathematical Problems in Engineering
Volume 2017, Article ID 7490879, 8 pages
Research Article

Exploiting Query’s Temporal Patterns for Query Autocompletion

Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Hunan, China

Correspondence should be addressed to Danyang Jiang; nc.ude.tdun@gnaijgnaynad

Received 18 September 2016; Accepted 5 March 2017; Published 23 March 2017

Academic Editor: Emilio Insfran

Copyright © 2017 Danyang Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Query autocompletion (QAC) is a common interactive feature of web search engines. It aims at assisting users to formulate queries and avoiding spelling mistakes by presenting them with a list of query completions as soon as they start typing in the search box. Existing QAC models mostly rank the query completions by their past popularity collected in the query logs. For some queries, their popularity exhibits relatively stable or periodic behavior while others may experience a sudden rise in their query popularity. Current time-sensitive QAC models focus on either periodicity or recency and are unable to respond swiftly to such sudden rise, resulting in a less optimal QAC performance. In this paper, we propose a hybrid QAC model that considers two temporal patterns of query’s popularity, that is, periodicity and burst trend. In detail, we first employ the Discrete Fourier Transform (DFT) to identify the periodicity of a query’s popularity, by which we forecast its future popularity. Then the burst trend of query’s popularity is detected and incorporated into the hybrid model with its cyclic behavior. Extensive experiments on a large, real-world query log dataset infer that modeling the temporal patterns of query popularity in the form of its periodicity and its burst trend can significantly improve the effectiveness of ranking query completions.