Conference proceeding
Web Query Prediction by Unifying Model
2008 IEEE International Conference on Data Mining Workshops, pp.436-441
12/2008
DOI: 10.1109/ICDMW.2008.53
Abstract
Recently, many commercial products, such as Google Trends and Yahoo! Buzz, are released to monitor the past search engine query frequency trend. However, little research has been devoted for predicting the upcoming query trend, which is of great importance in providing guidelines for future business planning. In this paper, a unified solution is presented for such a purpose. Besides the classical time series model, we propose to integrate the cosine signal hidden periodicities model to capture periodic information of query time series. Motivated by the fact that these models cannot capture the external accidental event factors which could significantly influence the query frequency, the query correlation model is also modified and integrated for predicting the upcoming query trend. Finally linear regression is utilized for model unification. Experiments based on 15,511,531 queries from a commercial search engine query log ranging within 283 days well validate the effectiveness of our proposed unified algorithm.
Details
- Title: Subtitle
- Web Query Prediction by Unifying Model
- Creators
- Ning Liu - Microsoft Research AsiaJ. Yan - Microsoft Research AsiaShuicheng Yan - National University of SingaporeWeiguo Fan - Virginia TechZheng Chen - Microsoft Research Asia
- Resource Type
- Conference proceeding
- Publication Details
- 2008 IEEE International Conference on Data Mining Workshops, pp.436-441
- Publisher
- IEEE
- DOI
- 10.1109/ICDMW.2008.53
- ISSN
- 2375-9232
- eISSN
- 2375-9259
- Language
- English
- Date published
- 12/2008
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380555902771
Metrics
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