Effect of snippet text bolding in search user behavior

doi:10.16511/j.cnki.qhdxxb.2018.25.033

Download: PDF(5709 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract Search users rely on result captions including titles, snippets and URLs to decide whether they should click a particular result. Snippets usually serve as a query-dependent summary of its corresponding landing page and are, therefore, one of the most important factors in the search interaction process. At present, commercial search engines use query bolding strategies, but these have various problems and lack useful information. This paper presents a bolding strategy that improves user search efficiency. The method includes three bolding strategies based on crowd sourcing results which differ from the query terms strategy. Tests show that the search behavior is affected by the term bolding strategies without changes in the snippet contents. The tests also show that the responses to the three bolding strategies are better than responses to the query terms bolding strategy to produce a better bolding strategy. The appropriate bolding numbers, bolding ratio, and targeted information have a very positive impact on the user's search behavior.

Keywords search user behavior text bolding search engine results page (SERP) search results summary

Issue Date: 15 August 2018

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	ZHANG Hui
	SU Ning
	LIU Yiqun
	MA Shaoping

Cite this article:

ZHANG Hui,SU Ning,LIU Yiqun, et al. Effect of snippet text bolding in search user behavior[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(8): 703-709.

URL:

http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.25.033 OR http://jst.tsinghuajournals.com/EN/Y2018/V58/I8/703

[1] EICHHORN G, KURTZ M J, ACCOMAZZI A, et al. The NASA astrophysics data system:The search engine and its user interface[J]. Astronomy and Astrophysics Supplement, 2000, 143(1):61-83.
[2] LEVY A Y, RAJARAMAN A, ORDILLE J J. Querying heterogeneous information sources using source descriptions[C]//Proceedings of the 22nd International Conference on Very Large Data Bases. San Francisco, CA, USA:Morgan Kaufmann Publishers Inc., 1996:251-262.
[3] PAEK T, DUMAIS S, LOGAN R. WaveLens:A new view onto internet search results[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Vienna, Austria:ACM, 2004:727-734.
[4] CUTRELL E, GUAN Z W. What are you looking for?:An eye-tracking study of information usage in web search[C]//Proceedings of SIGCHI Conference on Human Factors in Computing Systems. San Jose, CA, USA:ACM, 2007:407-416.
[5] KANUNGO T, ORR D. Predicting the readability of short web summaries[C]//Proceedings of the Second ACM International Conference on Web Search and Data Mining. Barcelona, Spain:ACM, 2009:202-211.
[6] CHEN Y, LIU Y Q, ZHOU K, et al. Does vertical bring more satisfaction?:Predicting search satisfaction in a heterogeneous environment[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne, Australia:ACM, 2015:1581-1590.
[7] BALDONADO M Q W, WINOGRAD T. Hi-cites:Dynamically created citations with active highlighting[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Los Angeles, CA, USA:ACM, 1998:408-415.
[8] JOACHIMS T, GRANKA L, PAN B, et al. Accurately interpreting clickthrough data as implicit feedback[C]//Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Salvador, Brazil:ACM, 2005:154-161.
[9] AULA A. Enhancing the readability of search result summaries[C]//Proceedings of the HCI 2004:Design for Life. Leeds, UK:HCI, 2004. https://www.researchgate.net/publication/239970095.
[10] KANUNGO T, ORR D. Predicting the readability of short web summaries[C]//Proceedings of the Second ACM International Conference on Web Search and Data Mining. Barcelona, Spain:ACM, 2009.
[11] KAISSER M, HEARST M A, LOWE J B. Improving search results quality by customizing summary lengths[C]//Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics:Human Language Technlogies. Columbus, Ohio, USA:ACL, 2008:701-709.
[12] FEILD H, WHITE R W, FU X. Supporting orientation during search result examination[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Paris, France:ACM, 2013:2999-3008.
[13] VARADARAJAN R, HRISTIDIS V. A system for query-specific document summarization[C]//Proceedings of the 15th ACM International Conference on Information and Knowledge Management. Arlington, Virginia, USA:ACM, 2006:622-631.
[14] ROSE D E, ORR D, KANTAMNENI R G P. Summary attributes and perceived search quality[C]//Proceedings of the 16th International Conference on World Wide Web. Banff, Alberta, Canada:ACM, 2007:1201-1202.
[15] KATE R J, LUO Z Q, PATWARDHAN S, et al. Learning to predict readability using diverse linguistic features[C]//Proceedings of the 23rd International Conference on Computational Linguistics. Beijing, China:ACM, 2010:546-554.
[16] KICKMEIER M D, ALBERT D. The effects of scannability on information search:An online experiment[C]//Proceedings of the 7th British HCI Group Annual Conference. Bath, UK:HCI, 2003.
[17] LANDAUER T, EGAN D, REMDE J, et al. Enhancing the usability of text through computer delivery and formative evaluation:The superbook project[M]. MCKNIGHT C, DILLON A, RICHARDSON J. Hypertext:A Psychological Perspective. New York:Ellis Horwood, 1993.
[18] BAUDISCH P, LEE B, HANNA L. Fishnet, a fisheye web browser with search term popouts:A comparative evaluation with overview and linear view[C]//Proceedings of 2004 Working Conference on Advanced Visual Interfaces. Gallipoli, Italy:ACM, 2004:133-140.
[19] FEW S. Now you see it:Simple visualization techniques for quantitative analysis[M]. Piedmont:Analytics Press, 2009.
[20] LUO C, LI X, KHODZHAEV A, et al. THUSAM at NTCIR-11 IMine task[C]//Proceedings of the 11th NTCIR Conference. Tokyo, Japan:NTCIR, 2014.
[21] COHEN J. Weighted kappa:Nominal scale agreement provision for scaled disagreement or partial credit[J]. Psychological Bulletin, 1968, 70(4):213-220.
[22] LORIGO L, HARIDASAN M, BRYNJARSDÓTTIR H, et al. Eye tracking and online search:Lessons learned and challenges ahead[J]. Journal of the Association for Information Science and Technology, 2008, 59(7):1041-1052.
[23] JIANG J P, AWADALLAH A H, SHI X L, et al. Understanding and predicting graded search satisfaction[C]//Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. Shanghai, China:ACM, 2015:57-66.