Time-sensitive query auto-completion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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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.