JACIII Vol.20 No.6 pp. 910-918
doi: 10.20965/jaciii.2016.p0910


Design of Context Search Engine Based on Analysis of User’s Search Intentions

Yasufumi Takama*, Yanjun Zhu*, Shogo Kori*, Koichi Yamaguchi*, Lieu-Hen Chen**, and Hiroshi Ishikawa*

*Graduate School of System Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
**College of Science and Technology, National Chi Nan University
#1 University Road, Puli, Nantao, Taiwan

March 20, 2016
July 20, 2016
Online released:
November 20, 2016
November 20, 2016
advanced search engine, information retrieval, user behavior, temporal variation

The context search engine has been studied for answering trend-related queries. As trend information is obtained from temporal data, which is common in many applications, the context engine is expected to be available regardless of domains. When using existing search engines, it is supposed that users submit a series of queries based on search intention. Therefore, search functions of the context search engine should be designed based on the user’s potential search intention. To analyze user’s behavior in information retrieval, this paper conducted experiments using existing Web search engines. The experimental result is analyzed, based on which the design of a context search engine is described. As another contribution of this paper, new types of temporal variations which can be used to specify queries of the context search engine are also proposed. The results of user experiments confirmed the usability of the proposed temporal variations.

  1. [1] Y. Zhu, Y. Takama, Y. Kato, S. Kori, and H. Ishikawa, “Introduction of Search Engine Focusing on Trend-related Queries to Market of Data,” MoDAT2014 in ICDM2014, pp. 512-516, 2014.
  2. [2] A. McCallum, K. Nigam, J. Rennie, and K. Seymore, “A machine learning approach to building domain-specific search engines,” IJCAI99, pp. 662-667, 1999.
  3. [3] S. Oyama, T. Kokubo, and T. Ishida, “Domain-specific web search with keyword spices,” IEEE Trans. on Knowledge and Data Engineering, Vol.16, No.1, pp. 17-27, 2004.
  4. [4] S. Kori, Y. Zhu, K. Yamaguchi, S. Takiguchi, and Y. Takama, “Analysis of User’s Behaviour Based on Search Intentions for Information Retrieval Using Search Engines,” TAAI2015, pp. 64-70, 2015.
  5. [5] S. Bajracharya, T. Ngo, E. Linstead, P. Rigor, Y. Dou, P. Baldi, and C. Lopes, “Sourcerer: a search engine for open source code supporting structure-based search,” Companion to the 21st ACM SIGPLAN Symp. on Object-Oriented Programming Systems, Languages, and Applications, pp. 681-682, 2006.
  6. [6] T. Kamei, A. monden and K. Matsumoto, “The Development of a Software Search Engine for the World Wide Web,” IEICE Technical Report, Vol.102, No.617, pp. 59-64, 2003 (in Japanese).
  7. [7] E. Agichtein, E. Brill, and S. Dumais, “Improving Web Search Ranking by Incorporating User Behavior Information,” Proc. of the 29th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 19-26, 2006.
  8. [8] H. Ma, H. Yang, I. King, and M. R. Lyu, “Learning latent semantic relations from clickthrough data for query suggestion,” Proc. of the 17th ACM Conf. on Information and Knowledge Management, pp. 709-718, 2008.
  9. [9] K. Sugiyama, K. Hatano, and M. Yoshikawa, “Adaptive Web Search Based on User Profile Constructed without Any Effort from Users,” Proc. of the 13th Int. Conf. on World Wide Web, pp. 675-684, 2004.
  10. [10] S. M. Beitzel, E. C. Jensen, A. Chowdhury, D. Grossman, and O. Frieder, “Hourly analysis of a very large topically categorized web query log,” SIGIR2004, pp. 321-328, 2004.
  11. [11] A. Spink, D. Wolfram, B. J. Jansen and T. Saracevic, “Searching the web: The public and their queries,” J. of the American Society for Information Science and Technology, Vol.52, No.3, pp. 226-234, 2001.
  12. [12] B. J. Jansen, A. Spink, J. Bateman and T. Saracevic, “Real life information retrieval: A study of user queries on the web,” SIGIR Forum, Vol.32, No.1, pp. 5-17, 1998.
  13. [13] C. Silverstein, M. Henzinger, H. Marais, and M. Moricz, “Analysis of a very large web search engine query log,” ACM SIGIR Forum, Vol.33, No.1, pp. 6-12, 1999.
  14. [14] A. Broder, “A taxonomy of web search,” ACM SIGIR Forum, Vol.36, No.2, pp. 3-10, 2002.
  15. [15] T. Kato, M. Matsushita, and N. Kando, “MuST: A workshop on multimodal summarization for trend information,” Proc. of the 5th NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering and CrossLingual Information Access, pp. 556-563, 2005.
  16. [16] H. Urokohara, K. Tanaka, K. Furuta, M. Honda, and M. Kurosu, “NEM: “novice expert ratio method” a usability evaluation method to generate a new performance measure,” CHI EA’00, pp. 185-186, 2000.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 28, 2017