Exploiting Query’s Temporal Patterns for Query Autocompletion

Abstract

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.