Paper Info
Title
Similarity of Temporal Query Logs Based on ARIMA Model
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.
Year
DOI
Venue
2006
10.1109/ICDM.2006.144
ICDM Workshops
Keywords
DocType
ISSN
better similarity measurement,arima temporal similarity,short text query,arima ts,query log,temporal query log,arima model,temporal query,similarity measurement,related query,arima coefficient,euclidean distance,search engines,databases,stochastic processes,search engine,frequency,time series,information retrieval,user requirements,data mining,satisfiability,predictive models
Conference
1550-4786
ISBN
Citations 
PageRank 
0-7695-2701-7
2
0.40
References 
Authors
4
6
Name
Order
Citations
PageRank
Ning Liu125315.62
Shuzhen Nong220.40
Jun Yan3179885.25
Benyu Zhang4213590.41
Zheng Chen55019256.89
Ying Li626521.64