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Research Article

Web Search Engine Results Page Viewing Formats for Different Search Tasks

ORCID Icon &
Received 08 Nov 2023, Accepted 01 Jul 2024, Published online: 29 Jul 2024

References

  • Agarwal, M. K., & Sahu, T. (2021). Lookup or exploratory: What is your search intent?. arXiv preprint arXiv:2110.04640
  • Ahmadian, S., Kazemi, M. (2023, November 3). Best of both worlds: Achieving scalability and quality in text clustering. Google Research. Retrieved March 15, 2024, from https://blog.research.google/2023/11/best-of-both-worlds-achieving.html/
  • Akinnubi, A., & Ajiboye, J. (2023). Knowledge graph: A survey. The International Journal of Robotics Research, 4(2), 366–377. https://doi.org/10.36227/techrxiv.22813967.v1
  • Alasmari, A., & Zhou, L. (2017). The effects of visualization and synchronization on clustered-based mobile web search. International Journal of Human–Computer Interaction, 33(6), 431–442. https://doi.org/10.1080/10447318.2017.1278894
  • Andrews, K., Gutl, C., Moser, J., Sabol, V., & Lackner, W. (2001, May). Search result visualisation with xfind [Paper presentation]. Proceedings Second International Workshop on User Interfaces in Data Intensive Systems. UIDIS 2001 (pp. 50–58). IEEE.
  • Baldonado, M. Q. W., & Winograd, T. (1998, January). Hi-cites: Dynamically created citations with active highlighting [Paper presentation]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 408–415). https://doi.org/10.1145/274644.274700
  • Broder, A. (2002, September). A taxonomy of web search. In ACM SIGIR Forum (Vol. 36, pp. 3–10). ACM. https://doi.org/10.1145/792550.792552
  • Carpineto, C., Romano, G., & Bordoni, F. U. (2004). Exploiting the potential of concept lattices for information retrieval with CREDO. Journal of Universal Computer Science, 10(8), 985–1013.
  • Carpineto, C., Mizzaro, S., Romano, G., & Snidero, M. (2009a). Mobile information retrieval with search results clustering: Prototypes and evaluations. Journal of the American Society for Information Science and Technology, 60(5), 877–895. https://doi.org/10.1002/asi.21036
  • Carpineto, C., Osiński, S., Romano, G., & Weiss, D. (2009b). A survey of web clustering engines. ACM Computing Surveys, 41(3), 1–38. https://doi.org/10.1145/1541880.1541884
  • Cellary, W., Wiza, W., & Walczak, K. (2004). Visualizing web search results in 3D. Computer Magazine, 37(5), 87–89. https://doi.org/10.1109/MC.2004.1297255
  • Chen, K. T. C. (2020). Searching strategies and reading strategies for English E-journal articles used by EFL graduate students. Education and Information Technologies, 25(2), 665–680. https://doi.org/10.1007/s10639-019-10007-3
  • Chua, C. (2012, November). A user interface guide for web search systems [Paper presentation]. Proceedings of the 24th Australian Computer-Human Interaction Conference (pp. 76–84). https://doi.org/10.1145/2414536.2414549
  • Clarke, C. L., Agichtein, E., Dumais, S., & White, R. W. (2007, July). The influence of caption features on clickthrough patterns in web search [Paper presentation]. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 135–142). https://doi.org/10.1145/1277741.1277767
  • Correll, S. J. (2001). Gender and the career choice process: The role of biased self-assessments. American Journal of Sociology, 106(6), 1691–1730. https://doi.org/10.1086/321299
  • Cutrell, E., & Guan, Z. (2007, April). What are you looking for? An eye-tracking study of information usage in web search. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 407–416).
  • Cutting, D. R., Karger, D. R., Pedersen, J. O., & Tukey, J. W. (1992, June). Scatter/Gather: A cluster-based approach to browsing large document collections [Paper presentation]. Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 318–329). https://doi.org/10.1145/133160.133214
  • Danaher, P. J., Mullarkey, G. W., & Essegaier, S. (2006). Factors affecting web site visit duration: A cross-domain analysis. Journal of Marketing Research, 43(2), 182–194. https://doi.org/10.1509/jmkr.43.2.182
  • Dame, N. (2015, February 27). Search engine land - What can businesses do about the knowledge graph dominating search results? http://searchengineland.com/businesses-knowledge-graph-dominating-search-results-215520.
  • Deng, Y., Zhang, W., Xu, W., Shen, Y., & Lam, W. (2023). Nonfactoid question answering as query-focused summarization with graph-enhanced multihop inference. IEEE Transactions on Neural Networks and Learning Systems, 1–15. https://doi.org/10.1109/TNNLS.2023.3258413
  • Dumais, S., Cutrell, E., & Chen, H. (2001, March). Optimizing search by showing results in context [Paper presentation]. Proceedings of the SIGCHI conference on Human Factors in Computing Systems (pp. 277–284). https://doi.org/10.1145/365024.365116
  • Egan, D. E., Remde, J. R., Gomez, L. M., Landauer, T. K., Eberhardt, J., & Lochbaum, C. C. (1989). Formative design evaluation of superbook. ACM Transactions on Information Systems, 7(1), 30–57. https://doi.org/10.1145/64789.64790
  • Ferragina, P., & Gulli, A. (2008). A personalized search engine based on web-snippet hierarchical clustering. Software: Practice and Experience, 38(2), 189–225. https://doi.org/10.1002/spe.829
  • Guha, R., McCool, R., & Miller, E. (2003, May). Semantic search [Paper presentation]. Proceedings of the 12th International Conference on World Wide Web (pp. 700–709). https://doi.org/10.1145/775152.775250
  • Hargittai, E., & Shafer, S. (2006). Differences in actual and perceived online skills: The role of gender. Social Science Quarterly, 87(2), 432–448. https://doi.org/10.1111/j.1540-6237.2006.00389.x
  • Hearst, M. (2009). Search user interfaces. Cambridge University Press.
  • Herman, I. (2009, November 12). W3C semantic web frequently asked questions. http://www.w3.org/2001/sw/SW-FAQ#swgoals.
  • Hoeber, O. (2014. March). Visual search analytics: Combining machine learning and interactive visualization to support human-centred search [Paper presentation]. MindTheGap@ iConference (pp. 37–43).
  • Hoeber, O. (2018, March). Information visualization for interactive information retrieval [Paper presentation]. Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (pp. 371–374). https://doi.org/10.1145/3176349.3176898
  • Hoeber, O., & Yang, X. D. (2006, December). A comparative user study of web search interfaces: HotMap, Concept Highlighter, and Google [Paper presentation]. 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings) (WI'06) (pp. 866–874). IEEE. https://doi.org/10.1109/WI.2006.6
  • Hoeber, O., & Yang, X. D. (2009). HotMap: Supporting visual exploration of Web search results. Journal of the American Society for Information Science and Technology, 60(1), 90–110. https://doi.org/10.1002/asi.20957
  • Hupfer, M. E., & Detlor, B. (2006). Gender and web information seeking: A self‐concept orientation model. Journal of the American Society for Information Science and Technology, 57(8), 1105–1115. https://doi.org/10.1002/asi.20379
  • Keikha, M., Park, J. H., & Croft, W. B. (2014)., July). Evaluating answer passages using summarization measures [Paper presentation]. Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 963–966). https://doi.org/10.1145/2600428.2609485
  • Kellar, M., Watters, C., & Shepherd, M. (2007). A field study characterizing web‐based information‐seeking tasks. Journal of the American Society for Information Science and Technology, 58(7), 999–1018. https://doi.org/10.1002/asi.20590
  • Kim, J. (2006). Task as a predictable indicator of information seeking behavior on the Web. Rutgers The State University of New Jersey - New Brunswick.
  • Koshman, S., Spink, A., & Jansen, B. J. (2006). Web searching on the Vivisimo search engine. Journal of the American Society for Information Science and Technology, 57(14), 1875–1887. https://doi.org/10.1002/asi.20408
  • Lagun, D., Hsieh, C. H., Webster, D., & Navalpakkam, V. (2014, July). Towards better measurement of attention and satisfaction in mobile search. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 113–122). https://doi.org/10.1145/2600428.2609631
  • Large, A., Beheshti, J., & Rahman, T. (2002). Gender differences in collaborative web searching behavior: An elementary school study. Information Processing & Management, 38(3), 427–443. https://doi.org/10.1016/S0306-4573(01)00034-6
  • Lewandowski, D., Drechsler, J., & Von Mach, S. (2012). Deriving query intents from web search engine queries. Journal of the American Society for Information Science and Technology, 63(9), 1773–1788. https://doi.org/10.1002/asi.22706
  • Li, W., Wang, S., Chen, X., Tian, Y., Gu, Z., Lopez-Carr, A., Schroeder, A., Currier, K., Schildhauer, M., & Zhu, R. (2023). Geographvis: A knowledge graph and geovisualization empowered cyberinfrastructure to support disaster response and humanitarian aid. ISPRS International Journal of Geo-Information, 12(3), 112. https://doi.org/10.3390/ijgi12030112
  • Li, Y., Zhao, J., Yang, L., & Zhang, Y. (2019, March). Construction, visualization and application of knowledge graph of computer science major [Paper presentation]. Proceedings of the 2019 International Conference on Big Data and Education (pp. 43–47). https://doi.org/10.1145/3322134.3322153
  • Liu, Z., Liu, Y., Zhou, K., Zhang, M., & Ma, S. (2015, August). Influence of vertical result in web search examination [Paper presentation]. Proceedings of the 38th International Acm Sigir Conference on Research and Development in Information Retrieval (pp. 193–202). https://doi.org/10.1145/2766462.2767714
  • Liu, Q., Li, Y., Duan, H., Liu, Y., & Qin, Z. (2016). Knowledge graph construction techniques. Journal of Computer Research and Development, 53(3), 582–600. https://doi.org/10.7544/issn1000-1239.2016.20148228
  • Lorigo, L., Pan, B., Hembrooke, H., Joachims, T., Granka, L., & Gay, G. (2006). The influence of task and gender on search and evaluation behavior using Google. Information Processing & Management, 42(4), 1123–1131. https://doi.org/10.1016/j.ipm.2005.10.001
  • Luo, J., Yao, S., Miao, K., Zhou, S., Feng, Y., & Xu, J. (2022, October). A knowledge graph visualization and retrieval system based on joint extraction model [Paper presentation]. 5th International Conference on Computer Information Science and Application Technology (CISAT 2022) (Vol. 12451, pp, 481–485). SPIE. https://doi.org/10.1117/12.2656590
  • Mangles, C. (2018, January 30). Search engine statistics 2018. (Smart Insights). https://www.smartinsights.com/search-engine-marketing/search-engine-statistics/.
  • Mann, T. M. (1999, September). Visualization of WWW-search results. In Proceedings of the Tenth International Workshop on Database and Expert Systems Applications. DEXA 99 (pp. 264–268). IEEE. https://doi.org/10.1109/DEXA.1999.795176
  • Marchionini, G. (2006). Exploratory search: From finding to understanding. Communications of the ACM, 49(4), 41–46. https://doi.org/10.1145/1121949.1121979
  • Mazurek, M., & Waldner, M. (2018). Visualizing expanded query results. Computer Graphics Forum, 37(3), 87–98. https://doi.org/10.1111/cgf.13403
  • Medcalc (2018). Cochran’s Q test. https://www.medcalc.org/manual/cochranq.php
  • Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019). Industry-scale knowledge graphs: Lessons and challenges: Five diverse technology companies show how it’s done. Queue, 17(2), 48–75. https://doi.org/10.1145/3329781.3332266
  • Purcell, K. (2011). Search and email still top the list of most popular online activities. http://www.pewinternet.org/2011/08/09/search-and-email-still-top-the-list-of-most-popular-online-activities/
  • Roy, M., & Chi, M. T. (2003). Gender differences in patterns of searching the web. Journal of Educational Computing Research, 29(3), 335–348. https://doi.org/10.2190/7BR8-VXA0-07A7-8AVN
  • Roy, M., Taylor, R., & Chi, M. T. (2003). Searching for information on-line and off-line: Gender differences among middle school students. Journal of Educational Computing Research, 29(2), 229–252. https://doi.org/10.2190/KCGA-3197-2V6U-WUTH
  • Roy, N., Maxwell, D., & Hauff, C. (2022, July). Users and Contemporary SERPs: A (Re-) Investigation [Paper presentation]. Proceedings of The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2765–2775). https://doi.org/10.1145/3477495.3531719
  • Russell, D. M., Tang, D., Kellar, M., & Jeffries, R. (2009, January). Task behaviors during web search: The difficulty of assigning labels. In 2009 42nd Hawaii International Conference on System Sciences (pp. 1–5). IEEE
  • Ruthven, I., & Kelly, D. (Eds.). (2011). Interactive information seeking, behaviour and retrieval. Facet Publishing.
  • Şendurur, E., & Yildirim, Z. (2015). Students’ web search strategies with different task types: An eye-tracking study. International Journal of Human-Computer Interaction, 31(2), 101–111. https://doi.org/10.1080/10447318.2014.959105
  • Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualization. In Proceedings of IEEE Symposium on Visual Languages (pp. 336–343).
  • Shokouhi, M., & Guo, Q. (2015, August). From queries to cards: Re-ranking proactive card recommendations based on reactive search history. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 695–704).
  • Singhal, A. (2012, May 16). Introducing the Knowledge Graph: Things, not strings. https://googleblog.blogspot.co.uk/2012/05/introducing-knowledge-graph-things-not.html
  • Toda, H., Kataoka, R., & Oku, M. (2007). Search result clustering using informatively named entities. International Journal of Human-Computer Interaction, 23(1-2), 3–23. https://doi.org/10.1080/10447310701360995
  • Tombros, A., & Sanderson, M. (1998, August). Advantages of query biased summaries in information retrieval [Paper presentation]. Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2–10). https://doi.org/10.1145/290941.290947
  • Verberne, S., van der Heijden, M., Hinne, M., Sappelli, M., Koldijk, S., Hoenkamp, E., & Kraaij, W. (2013). Reliability and validity of query intent assessments. Journal of the American Society for Information Science and Technology, 64(11), 2224–2237. https://doi.org/10.1002/asi.22948
  • Wang, C., Liu, Y., Zhang, M., Ma, S., Zheng, M., Qian, J., & Zhang, K. (2013, July). Incorporating vertical results into search click models [Paper presentation]. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 503–512). https://doi.org/10.1145/2484028.2484036
  • Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724–2743. https://doi.org/10.1109/TKDE.2017.2754499
  • Wang, Y., Yin, D., Jie, L., Wang, P., Yamada, M., Chang, Y., & Mei, Q. (2016, February). Beyond ranking: Optimizing whole-page presentation. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (pp. 103–112).
  • Zamir, O., & Etzioni, O. (1998, August). Web document clustering: A feasibility demonstration. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 46–54).
  • Zamir, O., & Etzioni, O. (1999). Grouper: A dynamic clustering interface to Web search results. Computer Networks, 31(11-16), 1361–1374. https://doi.org/10.1016/S1389-1286(99)00054-7
  • Zeng, H. J., He, Q., Liu, G., Chen, Z., Zhang, B., & Ma, W. Y. (2009). Query-based snippet clustering for search result grouping. (U.SPatent No7,617,176). U.S. Patent and Trademark Office.
  • Zhou, M. (2014). Gender difference in web search perceptions and behavior: Does it vary by task performance? Computers & Education, 78, 174–184. https://doi.org/10.1016/j.compedu.2014.06.005