Low-Level Activity Patterns as Indicators of User Familiarity with Websites
Authors: 
He Yu, 
Simon Harper, Markel VigoAuthors Info & Claims
He Yu, Simon Harper, Markel VigoAuthors Info & ClaimsPages 153 - 163
Abstract
Familiarity is a quality of user experience that has traditionally been difficult to define, capture, and quantify. Existing works on measuring familiarity with interactive systems have relied on surveys and self-reporting, which is obtrusive and prone to biases. Here, we propose a data-driven methodology to associate low-level activity patterns with familiarity. As a proof-of-concept, this methodology was tested on a website with 35,819 users over the course of 18 months, including 268 revisiting users who had reported their levels of familiarity with the platform. By using activity patterns as features of predictive models, we were able to successfully classify users with higher levels of familiarity with an accuracy of 82.7%. These results suggest that there is a relationship between user familiarity and activity patterns involving the exploration and use of navigational artefacts including breadcrumbs, navigation bars, and sidebar areas. This research opens up further opportunities for unobtrusively analysing the user experience on the Web.
References
[1]
George Aloysius and D. Binu. 2013. An approach to products placement in supermarkets using PrefixSpan algorithm. Journal of King Saud University - Computer and Information Sciences 25, 1 (jan 2013), 77–87. https://doi.org/10.1016/j.jksuci.2012.07.001
[2]
Aitor Apaolaza, Simon Harper, and Caroline Jay. 2015. Longitudinal Analysis of Low-Level Web Interaction through Micro Behaviours. In Proceedings of the 26th ACM Conference on Hypertext & Social Media(HT ’15). Association for Computing Machinery, New York, NY, USA, 337–340. https://doi.org/10.1145/2700171.2804453
[3]
Aitor Apaolaza and Markel Vigo. 2017. WevQuery: Testing Hypotheses about Web Interaction Patterns. Proc. ACM Hum.-Comput. Interact. 1, EICS, Article 4 (June 2017), 17 pages. https://doi.org/10.1145/3095806
[4]
James Blustein, Ishtiaq Ahmed, and Keith Instone. 2005. An Evaluation of Look-Ahead Breadcrumbs for the WWW. In Proceedings of the Sixteenth ACM Conference on Hypertext and Hypermedia(HYPERTEXT ’05). Association for Computing Machinery, New York, NY, USA, 202–204. https://doi.org/10.1145/1083356.1083394
[5]
Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (2006), 77–101. https://doi.org/10.1191/1478088706qp063oa
[6]
Lauren Bringman-Rodenbarger and Michael Hortsch. 2020. How students choose E-learning resources: the importance of ease, familiarity, and convenience. Faseb Bioadvances 2, 5 (2020), 286–295.
[7]
Harry Bruce, William Jones, and Susan Dumais. 2004. Information behaviour that keeps found things found.Information Research: An International Electronic Journal 10, 1(2004), n1.
[8]
Jengchung Victor Chen, Chinho Lin, David C. Yen, and Kyaw-Phyo Linn. 2011. The interaction effects of familiarity, breadth and media usage on web browsing experience. Computers in Human Behavior 27, 6 (2011), 2141–2152. https://doi.org/10.1016/j.chb.2011.06.008
[9]
Kuan-Ta Chen and Li-Wen Hong. 2007. User Identification Based on Game-Play Activity Patterns. In Proceedings of the 6th ACM SIGCOMM Workshop on Network and System Support for Games(NetGames ’07). Association for Computing Machinery, New York, NY, USA, 7–12. https://doi.org/10.1145/1326257.1326259
[10]
Michael J. Cole, Chathra Hendahewa, Nicholas J. Belkin, and Chirag Shah. 2015. User Activity Patterns During Information Search. ACM Trans. Inf. Syst. 33, 1, Article 1 (March 2015), 39 pages. https://doi.org/10.1145/2699656
[11]
Vagner Figueredo de Santana and Maria Cecília Calani Baranauskas. 2015. WELFIT: A remote evaluation tool for identifying Web usage patterns through client-side logging. International Journal of Human-Computer Studies 76 (2015), 40–49.
[12]
Alan R Dennis and Monica J Garfield. 2003. The adoption and use of GSS in project teams: Toward more participative processes and outcomes. MIS quarterly (2003), 289–323.
[13]
Himel Dev and Zhicheng Liu. 2017. Identifying Frequent User Tasks from Application Logs. In Proceedings of the 22Nd International Conference on Intelligent User Interfaces(IUI ’17). ACM, New York, NY, USA, 263–273. https://doi.org/10.1145/3025171.3025184
[14]
Carlos Flavián, Miguel Guinalíu, and Raquel Gurrea. 2006. The influence of familiarity and usability on loyalty to online journalistic services: The role of user experience. Journal of Retailing and Consumer Services 13, 5 (2006), 363–375.
[15]
Philippe Fournier Viger, Chun-Wei Lin, Uday Rage, Yun Sing Koh, and Rincy Thomas. 2017. A Survey of Sequential Pattern Mining. Data Science and Pattern Recognition 1 (02 2017), 54–77.
[16]
Philippe Fournier Viger, Cheng-Wei Wu, Antonio Gomariz, and Vincent Tseng. 2014. VMSP: Efficient Vertical Mining of Maximal Sequential Patterns. https://doi.org/10.1007/978-3-319-06483-3_8
[17]
Steve Fox, Kuldeep Karnawat, Mark Mydland, Susan Dumais, and Thomas White. 2005. Evaluating Implicit Measures to Improve Web Search. ACM Trans. Inf. Syst. 23, 2 (April 2005), 147–168. https://doi.org/10.1145/1059981.1059982
[18]
Dennis F Galletta, Raymond M Henry, Scott McCoy, and Peter Polak. 2006. When the wait isn’t so bad: The interacting effects of website delay, familiarity, and breadth. Information Systems Research 17, 1 (2006), 20–37.
[19]
David Gefen. 2000. E-commerce: the role of familiarity and trust. Omega 28, 6 (2000), 725–737.
[20]
Ruili Geng and Jeff Tian. 2015. Improving Web Navigation Usability by Comparing Actual and Anticipated Usage. IEEE Transactions on Human-Machine Systems 45, 1 (2015), 84–94. https://doi.org/10.1109/THMS.2014.2363125
[21]
Ranjay Gulati. 1995. Does familiarity breed trust? The implications of repeated ties for contractual choice in alliances. Academy of management journal 38, 1 (1995), 85–112.
[22]
Guo Haixiang, Yijing Li, Jennifer Shang, Gu Mingyun, Huang Yuanyue, and Bing Gong. 2016. Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications 73 (12 2016). https://doi.org/10.1016/j.eswa.2016.12.035
[23]
Eelco Herder. 2005. Characterizations of User Web Revisit Behavior. In LWA. Citeseer, 32–37.
[24]
David M. Hilbert and David F. Redmiles. 2000. Extracting usability information from user interface events. Comput. Surveys 32, 4 (dec 2000), 384–421. https://doi.org/10.1145/371578.371593
[25]
Rosie Jones and Kristina Lisa Klinkner. 2008. Beyond the Session Timeout: Automatic Hierarchical Segmentation of Search Topics in Query Logs. In Proceedings of the 17th ACM Conference on Information and Knowledge Management(CIKM ’08). Association for Computing Machinery, New York, NY, USA, 699–708. https://doi.org/10.1145/1458082.1458176
[26]
Bahar Kaya, Elaheh Behravesh, A. Mohammed Abubakar, Omer Sami Kaya, and Carlos Orús. 2019. The Moderating Role of Website Familiarity in the Relationships Between e-Service Quality, e-Satisfaction and e-Loyalty. Journal of Internet Commerce 18, 4 (2019), 369–394. https://doi.org/10.1080/15332861.2019.1668658 arXiv:https://doi.org/10.1080/15332861.2019.1668658
[27]
Jun H. Kim, Daniel V. Gunn, Eric Schuh, Bruce Phillips, Randy J. Pagulayan, and Dennis Wixon. 2008. Tracking Real-Time User Experience (TRUE): A Comprehensive Instrumentation Solution for Complex Systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’08). Association for Computing Machinery, New York, NY, USA, 443–452. https://doi.org/10.1145/1357054.1357126
[28]
Yonnim Lee and Ohbyung Kwon. 2011. Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services. Electronic Commerce Research and Applications 10, 3(2011), 342–357. https://doi.org/10.1016/j.elerap.2010.11.005
[29]
Zhenyu Li, Gaogang Xie, Mohamed Ali Kaafar, and Kave Salamatian. 2015. User Behavior Characterization of a Large-Scale Mobile Live Streaming System. In Proceedings of the 24th International Conference on World Wide Web(WWW ’15 Companion). Association for Computing Machinery, New York, NY, USA, 307–313. https://doi.org/10.1145/2740908.2743054
[30]
Bonnie Lida, Spring Hull, and Katie Pilcher. 2003. Breadcrumb navigation: An exploratory study of usage. Usability News 5, 1 (2003), 1–7.
[31]
Niklas Luhmann and Christian Morgner. 2019. Trust And Power Luhmann.
[32]
Carlos A Maldonado and Marc L Jlesnick. 2002. Do common user interface design patterns improve navigation?. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 46. SAGE Publications Sage CA: Los Angeles, CA, 1315–1319.
[33]
Jorge Maldonado, Mar Pérez-Sanagustín, Pedro Moreno-Marcos, Carlos Alario-Hoyos, Pedro Merino, and Carlos Delgado-Kloos. 2018. Predicting Learners’ Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning: 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3-5, 2018, Proceedings. 355–369. https://doi.org/10.1007/978-3-319-98572-5_27
[34]
Carl H Mooney and John F Roddick. 2013. Sequential pattern mining–approaches and algorithms. ACM Computing Surveys (CSUR) 45, 2 (2013), 1–39.
[35]
Richard L Moreland and Robert B Zajonc. 1982. Exposure effects in person perception: Familiarity, similarity, and attraction. Journal of Experimental Social Psychology 18, 5 (1982), 395–415.
[36]
Sougata Mukherjea and James D. Foley. 1995. Visualizing the World-Wide Web with the Navigational View Builder. Comput. Netw. ISDN Syst. 27, 6 (apr 1995), 1075–1087. https://doi.org/10.1016/0169-7552(95)00023-Z
[37]
Ajaya Neupane, Nitesh Saxena, and Leanne Hirshfield. 2017. Neural Underpinnings of Website Legitimacy and Familiarity Detection: An FNIRS Study. In Proceedings of the 26th International Conference on World Wide Web(WWW ’17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1571–1580. https://doi.org/10.1145/3038912.3052702
[38]
Hartmut Obendorf, Harald Weinreich, Eelco Herder, and Matthias Mayer. 2007. Web Page Revisitation Revisited: Implications of a Long-Term Click-Stream Study of Browser Usage. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’07). Association for Computing Machinery, New York, NY, USA, 597–606. https://doi.org/10.1145/1240624.1240719
[39]
Travis E Oliphant. 2006. A guide to NumPy. Vol. 1. Trelgol Publishing USA.
[40]
Joonah Park and Jinwoo Kim. 2000. Effects of Contextual Navigation Aids on Browsing Diverse Web Systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’00). Association for Computing Machinery, New York, NY, USA, 257–264. https://doi.org/10.1145/332040.332442
[41]
Jane Quigley, David R Peck, Sara Rutter, and EM Williams. 2002. Making choices: factors in the selection of information resources among science faculty at the University of Michigan. Results of a survey conducted July–September, 2000. Issues in Science and Technology Librarianship 34 (2002).
[42]
Bonnie Lida Rogers and Barbara Chaparro. 2003. Breadcrumb navigation: Further investigation of usage. Usability News 5, 2 (2003), 1–7.
[43]
Benjamin Rosman, Subramanian Ramamoorthy, M.M. Hassan Mahmud, and Pushmeet Kohli. 2014. On User Behaviour Adaptation under Interface Change. In Proceedings of the 19th International Conference on Intelligent User Interfaces(IUI ’14). Association for Computing Machinery, New York, NY, USA, 273–278. https://doi.org/10.1145/2557500.2557535
[44]
Roy A. Ruddle. 2009. How Do People Find Information on a Familiar Website?. In Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology(BCS-HCI ’09). BCS Learning & Development Ltd., Swindon, GBR, 262–268.
[45]
Miriam Santos, Jastin Soares, Pedro Henriques Abreu, Helder Araujo, and Joao Santos. 2018. Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches. IEEE Computational Intelligence Magazine 13 (10 2018), 59–76. https://doi.org/10.1109/MCI.2018.2866730
[46]
Dan E. Tamir, Oleg V. Komogortsev, Carl J. Mueller, Divya K. D. Venkata, Gregory R. LaKomski, and Arwa. M. Jamnagarwala. 2011. Detection of Software Usability Deficiencies. In Design, User Experience, and Usability. Theory, Methods, Tools and Practice, Aaron Marcus (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 527–536.
[47]
Linda Tauscher and Saul Greenberg. 1997. How people revisit web pages: Empirical findings and implications for the design of history systems. International Journal of Human-Computer Studies 47, 1 (1997), 97–137.
[48]
Hui Teng. 2005. Location breadcrumbs for navigation: An exploratory study. Master’s thesis, Dalhousie University, NS, Canada., 2003. Faculty of ….
[49]
Paul Thomas. 2014. Using Interaction Data to Explain Difficulty Navigating Online. ACM Trans. Web 8, 4, Article 24 (Nov. 2014), 41 pages. https://doi.org/10.1145/2656343
[50]
Stella Tomasi, David Schuff, and Ozgur Turetken. 2018. Understanding novelty: how task structure and tool familiarity moderate performance. Behaviour & Information Technology 37, 4 (2018), 406–418. https://doi.org/10.1080/0144929X.2018.1441325 arXiv:https://doi.org/10.1080/0144929X.2018.1441325
[51]
Wilko van Hoek and Zeljko Carevic. 2017. Building User Groups Based on a Structural Representation of User Search Sessions. In Research and Advanced Technology for Digital Libraries, Jaap Kamps, Giannis Tsakonas, Yannis Manolopoulos, Lazaros Iliadis, and Ioannis Karydis (Eds.). Springer International Publishing, Cham, 459–470.
[52]
Markel Vigo and Simon Harper. 2017. Real-time detection of navigation problems on the World ‘Wild’Web. International Journal of Human-Computer Studies 101 (2017), 1–9.
[53]
Harald Weinreich, Hartmut Obendorf, Eelco Herder, and Matthias Mayer. 2006. Off the beaten tracks: exploring three aspects of web navigation. In Proceedings of the 15th international conference on World Wide Web. 133–142.
[54]
John D Wells, William L Fuerst, and Jonathan W Palmer. 2005. Designing consumer interfaces for experiential tasks: an empirical investigation. European Journal of Information Systems 14, 3 (2005), 273–287.
[55]
Bruce WA Whittlesea. 1993. Illusions of familiarity.Journal of Experimental Psychology: Learning, Memory, and Cognition 19, 6(1993), 1235.
[56]
Iris Xie and Soohyung Joo. 2009. Selection of information sources: Accessibility of and familiarity with sources, and types of tasks. Proceedings of the American Society for Information Science and Technology 46, 1 (2009), 1–18. https://doi.org/10.1002/meet.2009.1450460215 arXiv:https://asistdl.onlinelibrary.wiley.com/doi/pdf/10.1002/meet.2009.1450460215
[57]
Andrew P Yonelinas. 2002. The nature of recollection and familiarity: A review of 30 years of research. Journal of memory and language 46, 3 (2002), 441–517.
[58]
He Yu, Simon Harper, and Markel Vigo. 2021. Modeling micro-interactions in self-regulated learning: A data-driven methodology. International Journal of Human-Computer Studies 151 (2021), 102625. https://doi.org/10.1016/j.ijhcs.2021.102625
[59]
Jie Zhang, Ali A. Ghorbani, and Robin Cohen. 2006. An Improved Familiarity Measurement for Formalization of Trust in E-Commerce Based Multiagent Systems. In Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services(PST ’06). Association for Computing Machinery, New York, NY, USA, Article 19, 8 pages. https://doi.org/10.1145/1501434.1501458
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- Low-Level Activity Patterns as Indicators of User Familiarity with Websites
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360 pages
ISBN:9781450392075
DOI:10.1145/3503252
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