Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Evaluating real-time search query variation for intelligent information retrieval service

지능 정보검색 서비스를 위한 실시간검색어 변화량 평가

  • 정민영 (광주여자대학교 식품영양학과)
  • Received : 2018.11.08
  • Accepted : 2018.12.20
  • Published : 2018.12.28
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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.

포털 사이트의 핵심 서비스인 검색서비스는 입력되는 검색어 중에서 짧은 순간에 급상승하는 검색어를 대상으로 순간 검색빈도가 높은 것을 기준으로 순위별로 제시하는 것이므로 일정기간 동안 관심도가 높은 검색어를 곧바로 알려주기는 힘들다. 따라서 이를 극복하고 검색어 변화에 대한 향상된 분석결과가 나오게 하여 보다 지능적인 정보검색 서비스를 제공하기 위한 노력이 필요하다. 이를 위하여 본 논문에서는 실시간검색어의 관심도와 지속도, 그리고 주목도를 측정할 수 있는 기준을 제시한다. 그리고 그 기준에 맞추어 일정기간 동안 시간, 일간, 주간, 월간 실시간검색어에 대한 변화의 측정과 집계를 하고 이를 통해 관심도가 높은 이슈, 관심이 길게 지속된 이슈, 변화가능성이 커서 앞으로 주목해야 할 이슈를 평가한다.

Keywords

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Fig. 1. 4-week score change by week for top 10 interest keywords

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Fig. 2. Last week score change by day for top 10 interest keywords

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Fig. 3. Last day score change by hour for top 10 interest keywords

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Fig. 4. 4-week score change by week for top 10 continuity keywords

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Fig. 5. Last week score change by day for top 10 continuity keywords

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Fig. 6. Last day score change by hour for top 10 continuity keywords

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Fig. 7. 4-week change rate per week for top 10 continuity keywords

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Fig. 8. Average change rate per week for top 10 continuity keywords

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Fig. 9. Last week change rate per day for top 10 continuity keywords

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Fig. 10. Average change rate per day for top 10 continuity keywords

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Fig. 11. Last day change rate per hour for top 10 continuity keywords

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Fig. 12. Average change rate per hour for top 10 continuity keywords

References

  1. N. W. Jung & S. W. Kim. (2017). High-speed internet service as Universal service. Journal of Digital Convergence, 15(2), 11-25. DOI : 10.14400/JDC.2017.15.2.11
  2. H. Jiang, A. Yang, F. Yan & H. Miao. (2016). Research on Pattern Analysis and Data Classification Methodology for Data Mining and Knowledge Discovery, International Journal of Hybrid Information Technology, 9(3), 179-188. DOI : 10.14257/ijhit.2016.9.3.17
  3. A. Abbasi, S. Sarker & Roger. H. L. Chiang. (2016). Big Data Research in Information Systems: Toward an Inclusive Research Agenda, Journal of the Association for Information Systems, 17(2), 1-32. DOI : 10.17705/1jais.00423
  4. S. H. Hong. (2016). New Authentication Methods based on User's Behavior Big Data Analysis on Cloud. Journal of Convergence for SMB, 6(4), 31-36. DOI : 10.22156/CS4SMB.2016.6.4.031
  5. J. H. Ku. (2017). A Study on the Machine Learning Model for Product Faulty Prediction in Internet of Things Environment, Journal of Convergence for SMB, 7(1), 31-36. DOI : 10.22156/CS4SMB.2017.7.1.055
  6. P. Imire & P. Bednar. (2013). Virtual Personal Assistant, ItAIS 2013, Proceedings of 10th Conference of the Italian Chapter of AIS, 1-8.
  7. R. Knote, A. Janson, L. Eigenbrod & M. Sollner. (2018). The What and How of Smart Personal Assistants: Principles and Application Domains for IS Reserach, Multikonferenz Wirtschaftsinformatik (MKWI). Luneburg, Germany.
  8. X. Wu, X. Zhu, G. Q. Wu & W. Ding. (2014). Data mining with big data, IEEE transactions on knowledge and data engineering, 26(1), 97-107. DOI : 10.1109/TKDE.2013.109
  9. S. K. Kim, S. J Lee & J. G. Kim. (2016). Study on the Development of Phased Big Data Distribution Model Based on Big Data Distribution Ecology, Journal of Digital Convergence, 14(5), 95-106. DOI : 110.14400/JDC.2016.14.5.95
  10. S. H. Namn. (2015). Knowledge Creation Structure of Big Data Research Domain, Journal of Digital Convergence, 13(9), 129-136. DOI : 10.14400/JDC.2015.13.9.129
  11. Naver Search Help. (2015). Realtime hot searches, https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1989
  12. Daum Search Help. (2016). Realtime hot issues http://cs.daum.net/faq/15/14957.html#28971
  13. KISO Validation Committee. (2015). The fourth validation report about realtime hot searches of Naver.
  14. M. Y Chong. (2015). Selecting a key issue through association analysis of realtime search words, Journal of Digital Convergence, 13(12), 161-169. DOI : 10.14400/JDC.2015.13.12.161
  15. M. Y. Chong. (2016). Extracting week key issues and analyzing differences from realtime search keywords of portal sites, Journal of Digital Convergence, 14(12), 237-243. DOI : 10.14400/JDC.2016.14.12.237
  16. M. Y. Chong. (2017). Predicting changes of realtime search words using time series analysis and artificial neural networks, Journal of Digital Convergence, 15(12), 333-340. DOI : 110.14400/JDC.2017.15.12.333
  17. W. G. Kang, E. S. Ko, H. R. Lee & J. N. Kim. (2018). A Study of the Consumer Major Perception of Packaging Using Big Data Analysis - Focusing on Text Mining and Semantic Network Analysis, Journal of the Korea Convergence Society, 9(4), 15-22. DOI : 10.15207/JKCS.2018.9.4.015
  18. B. H. Shin & H. K. Jeon. (2017). Extracting Method of User's Interests by Using SNS Follower's Relationship and Sequential Pattern Evaluation Indices for Keyword, Journal of the Korea Convergence Society, 8(8), 71-75. DOI : 10.15207/JKCS.2017.8.8.071
  19. J. Starkweather. (2014). Introduction to basic Text Mining in R, University of North Texas.