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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (4): 9-19    DOI: 10.11925/infotech.2096-3467.2017.04.02
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Analyzing Dynamic Informational, Navigational and Transactional Online Queries
Xiaoojuan Zhang()
School of Computer and Information Science, Southwest University, Chongqing 400715, China
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Abstract  

[Objective] This paper aims to improve the performance of search engines optimization through analyzing dynamic informational, navigational and transactional online queries. [Methods] First, the author analyzed user intentions with queries, Web documents and the information needs. Second, for each category of query intention, this paper investigated the changing of Web documents and information needs for different trending queries. [Results] The distribution of popular informational, transactional and navigational queries were different. The informational queries were more dependent on Web documents and needs than the other two types of queries. [Limitations] The data for this study was collected in 29 days. More research is needed to automatically identify and aggregate the popular queries. [Conclusions] Search engines need to list diversified results for informational queries. They need to keep the relevant pages on the first page for navigational queries, maintain the original ranking of relevant pages for the user behavior-related queries, and improve the novelty of results for the entertainment-related queries.

Key wordsInformational Query      Transactional Query      Navigational Query      Query Dynamic      Information Need Dynamic      Document Content Dynamic     
Received: 07 November 2016      Published: 24 May 2017

Cite this article:

Xiaoojuan Zhang. Analyzing Dynamic Informational, Navigational and Transactional Online Queries. Data Analysis and Knowledge Discovery, 2017, 1(4): 9-19.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.04.02     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I4/9

[1] Broder A.A Taxonomy of Web Search[J]. SIGIR Forum, 2002, 36(2) : 3-10.
[2] 伍大勇, 赵世奇, 刘挺, 等. 融合多类特征的Web查询意图识别[J]. 模式识别与人工智能, 2012, 25(3): 500-505.
[2] (Wu Dayong, Zhao Shiqi, Liu Ting, et al. Identification of Query Intent via Combining Multiple Features[J]. Pattern Recognition and Artificial Intelligence, 2012,25(3): 500-505).
[3] Figueroa A.Exploring Effective Features for Recognizing the User Intent Behind Web Queries[J]. Computers in Industry, 2015, 68: 162-169.
[4] Zamora J, Mendoza M, Allende E.Query Intent Detection Based on Query Log Mining[J]. Journal of Web Engineering, 2014, 13(1): 24-52.
[5] Kulkarni A, Teevan J, Svore K M, et al.Understanding Temporal Query Dynamics[C]// Proceedings of the 4th International Conference on Web Search and Web Data Mining, Hong Kong, China. 2011.
[6] Fujii A.Modeling Anchor Text and Classifying Queries to Enhance Web Document Retrieval[C]//Proceedings of the 17th International Conference on World Wide Web. 2008.
[7] Craswell N, Hawking D, Robertson S.Effective Site Finding Using Link Anchor Information[C]// Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2001: 250-257.
[8] Ali S, Gul S, Gorman, G E.Search Engine Effectiveness Using Query Classification: A Study[J]. Online Information Review, 2016, 4(40): 515-528.
[9] Beitzel S M, Jensen E C, Chowdhury A, et al.Hourly Analysis of a Very Large Topically Categorized Web Query Log[C]//Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2004: 321-328.
[10] Vlachos M, Meek C, Vagena Z. Identifying Similarities, Periodicities and Bursts for Online Search Queries[C]// Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data. 2004:131-142.
[11] Ginsberg J, Mohebbi M H, Patel R S, et al.Detecting Influenza Epidemics Using Search Engine Query Data[J]. Nature, 2009, 457(7232): 1012-1014. DOI: 10.1038/nature 07634.
[12] Adar E, Weld D, Bershad B, et al.Why We Search: Visualizing and Predicting User Behavior[C]// Proceedings of the 16th International Conference on World Wide Web. 2007: 161-170.
[13] Johansson F, F?rdig T, Jethava V, et al.Intent-aware Temporal Query Modeling for Keyword Suggestion[C]// Proceedings of the 21st ACM International Confenrence on Information and Knowledge Managent. 2012: 83-86.
[14] Whilting S, McMinn A J, Jose J M. Exploring Real-Time Temporal Query Auto-Completion[C]// Proceedings of the 13th Dutch-Belgain Workshop on Information Retrieval. 2013: 12-15.
[15] Alonso O, Baeza-Yates R, Gertz G.Effectiveness of Temporal Snippets[C]//Proceedings of the 18th International Conference on World Wide Web. 2009.
[16] Berberich K, Bedathur S.Temporal Diversification of Search Results[C]// Proceedings of the SIGIR 2013 Workshop on Time-aware Information Access. 2013.
[17] Cho J, Garcia-Molina H.The Evolution of the Web and Implications for an Incremental Crawler[C]// Proceedings of the 26th International Conference on Very Large Databases. 2000.
[18] Fetterly D, Manasse M, Najork M, et al.A Large-scale Study of the Evolution of Web pages[C]// Proceedings of the 18th International Conference on World Wide Web. 2003.
[19] Ntoulas A, Cho J, Olston C.What’s New on the Web? The Evolution of the Web from a Search Engine Perspective[C]// Proceedings of the 13th International Conference on World Wide Web. 2004.
[20] Kim S J, Lee S H.An Empirical Study on the Change of Web Pages[A]// Web Technologies Research and Development[M]. Springer Berlin Heidelberg, 2004: 632-642.
[21] Cho J, Roy S, Adams R E.Page Quality: In Search of an Unbiased Web Ranking[C]// Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data. 2005: 551-562.
[22] Adar E, Teevan J, Dumais S T, et al.The Web Changes Everything: Understanding the Dynamics of Web Content[C]// Proceedings of the 2nd ACM International Conference on Web Search and Data Mining. 2009.
[23] Kausar A, Dhaka V S, Singh S K.A Novel Web Page Change Detection Approach Using SQL Server[J]. Journal of Modern Educatin and Computer Science, 2015, 9(7): 36-43.
[24] Alonso O, Gertz M.Clustering of Search Results Using Temporal Attributes[C]// Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, USA. 2006.
[25] Alfonseca E, Ciaramita M, Hall H, et al.Lexical Relationships from Temporal Patterns of Web Search Queries[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. 2009.
[26] Dakka W, Gravano L, Ipeirotis P G.Answering General Time Sensitive Queries[C]//Proceedings of the ACM 17th Conference on Information and Knowledge Management. 2008.
[27] Zahedi M, Aleahmad A, Rahgozar M, et al.Time Sensitive Blog Retrieval Using Temporal Properties of Queries[J]. Journal of Information Science, 2015, 43(1): 1-19. DOI: 10.1177/0165551515618589.
[28] Elsas J, Dumais S T.Leveraging Temporal Dynamics of Document Content in Relevance Ranking[C]// Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. 2010: 1-10.
[29] Syed U, Slivkins A, Mishra N.Adapting to the Shifting Intent of Search Queries[C]// Proceedings of the 23rd Annual Conference on Neural Information Processing Systems. 2009.
[30] Broder A Z, Glassman S C, Manasse M S.Syntactic Clustering of the Web[J]. Journal of Computer Networks and ISDN Systems, 1997,29(8-13): 1157-1166.
[31] Ozmutl H C, Spink A, Ozmutlu S.Analysis of Large Data Logs: An Application of Poisson Sampling on Excite Web Queries[J]. Information Processing & Management, 2002, 38(4): 473-490.
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