Document (#40317)

Author
Ren, P.
Chen, Z.
Ma, J.
Zhang, Z.
Si, L.
Wang, S.
Title
Detecting temporal patterns of user queries
Source
Journal of the Association for Information Science and Technology. 68(2017) no.1, S.113-128
Year
2017
Abstract
Query classification is an important part of exploring the characteristics of web queries. Existing studies are mainly based on Broder's classification scheme and classify user queries into navigational, informational, and transactional categories according to users' information needs. In this article, we present a novel classification scheme from the perspective of queries' temporal patterns. Queries' temporal patterns are inherent time series patterns of the search volumes of queries that reflect the evolution of the popularity of a query over time. By analyzing the temporal patterns of queries, search engines can more deeply understand the users' search intents and thus improve performance. Furthermore, we extract three groups of features based on the queries' search volume time series and use a support vector machine (SVM) to automatically detect the temporal patterns of user queries. Extensive experiments on the Million Query Track data sets of the Text REtrieval Conference (TREC) demonstrate the effectiveness of our approach.
Content
Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23578/full.
Theme
Suchtaktik

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