Similarity of Temporal Query Logs Based on ARIMA Model

  • Authors:

  • Ning Liu;Shuzhen Nong;Jun Yan;Benyu Zhang;Zheng Chen;Ying Li

  • Affiliations:

  • Microsoft Research Asia, China;Microsoft AdCenter, USA;Microsoft Research Asia, China;Microsoft Research Asia, China;Microsoft Research Asia, China;Microsoft AdCenter, USA

  • Venue:
  • ICDM '06 Proceedings of the Sixth International Conference on Data Mining
  • Year:
  • 2006

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Abstract

A challenging issue faced by modern information retrieval is that of determining and satisfying users' requirements relying only on very short text queries. In this paper, we propose an algorithm to find out related queries based on Auto-Regressive Integrated Moving Average (ARIMA) Model. First, we select and estimate ARIMA model of the temporal query logs. And then each query is denoted by a sequence of coefficients. We use the correlation of ARIMA coefficients as the similarity measurement. We call it as the ARIMA Temporal Similarity (ARIMA TS). This similarity describes how strongly two time series are linearly related. On the other hand, the ARIMA model could also be treated as a dimensionality reduction procedure. It can save storage space for a large database of the query logs. In addition, ARIMA model could be used as a tool to predict the trend of a query. The experimental results on two query logs of MSN search engine 1 demonstrate that the proposed approach can achieve better similarity measurement efficiently.