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http://dx.doi.org/10.14400/JDC.2018.16.12.335

Evaluating real-time search query variation for intelligent information retrieval service  

Chong, Min-Young (Department of Food and Nutrition, Gwangju Women's University)
Publication Information
Journal of Digital Convergence / v.16, no.12, 2018 , pp. 335-342 More about this Journal
Abstract
The search service, which is a core service of the portal site, presents search queries that are rapidly increasing among the inputted search queries based on the highest instantaneous search frequency, so it is difficult to immediately notify a search query having a high degree of interest for a certain period. Therefore, it is necessary to overcome the above problems and to provide more intelligent information retrieval service by bringing improved analysis results on the change of the search queries. In this paper, we present the criteria for measuring the interest, continuity, and attention of real-time search queries. In addition, according to the criteria, we measure and summarize changes in real-time search queries in hours, days, weeks, and months over a period of time to assess the issues that are of high interest, long-lasting issues of interest, and issues that need attention in the future.
Keywords
Real-time search queries; Real-time issues; Bigdata analysis; Text mining; Web mining; AI assistant;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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