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Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2702-7
pp: 366-370
Ning Liu , Microsoft Research Asia, Beijing, 100080, P.R.China
Shuzhen Nong , Microsoft AdCenter, Redmond, WA98052, U.S.A.
Jun Yan , Microsoft Research Asia, Beijing, 100080, P.R.China
Benyu Zhang , Microsoft Research Asia, Beijing, 100080, P.R.China
Zheng Chen , Microsoft Research Asia, Beijing, 100080, P.R.China
Ying Li , Microsoft Research Asia, Beijing, 100080, P.R.China
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 demonstrate that the proposed approach can achieve better similarity measurement efficiently
INDEX TERMS
autoregressive moving average processes, query processing
CITATION

N. Liu, S. Nong, J. Yan, B. Zhang, Z. Chen and Y. Li, "Similarity of Temporal Query Logs Based on ARIMA Model," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2007, pp. 366-370.
doi:10.1109/ICDMW.2006.147
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