Time-sensitive query auto-completion

  • Authors:

  • Milad Shokouhi;Kira Radinsky

  • Affiliations:

  • Microsoft Research, Cambridge, United Kingdom;Technion, Israel Institute of Technology, Haifa, Israel

  • Venue:
  • SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2012

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Abstract

Query auto-completion (QAC) is a common feature in modern search engines. High quality QAC candidates enhance search experience by saving users time that otherwise would be spent on typing each character or word sequentially. Current QAC methods rank suggestions according to their past popularity. However, query popularity changes over time, and the ranking of candidates must be adjusted accordingly. For instance, while halloween might be the right suggestion after typing ha in October, harry potter might be better any other time. Surprisingly, despite the importance of QAC as a key feature in most online search engines, its temporal dynamics have been under-studied. In this paper, we propose a time-sensitive approach for query auto-completion. Instead of ranking candidates according to their past popularity, we apply time-series and rank candidates according their forecasted frequencies. Our experiments on 846K queries and their daily frequencies sampled over a period of 4.5 years show that predicting the popularity of queries solely based on their past frequency can be misleading, and the forecasts obtained by time-series modeling are substantially more reliable. Our results also suggest that modeling the temporal trends of queries can significantly improve the ranking of QAC candidates.