Please use this identifier to cite or link to this item:
|Title:||Web query prediction by unifying model||Authors:||Liu, N.
|Issue Date:||2008||Citation:||Liu, N.,Yan, J.,Yan, S.,Fan, W.,Chen, Z. (2008). Web query prediction by unifying model. Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 : 436-441. ScholarBank@NUS Repository. https://doi.org/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. © 2008 IEEE.||Source Title:||Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008||URI:||http://scholarbank.nus.edu.sg/handle/10635/72187||ISBN:||9780769535036||DOI:||10.1109/ICDMW.2008.53|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 25, 2021
checked on Jul 15, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.