Abstract
Web pages are composed of different kinds of elements (menus, adverts, etc.). Segmenting pages into their elements has long been important in understanding how people experience those pages and in making those experiences “better.” Many approaches have been proposed that relate the resultant elements with the underlying source code; however, they do not consider users’ interactions. Another group of approaches analyses eye movements of users to discover areas that interest or attract them (i.e., areas of interest or AOIs). Although these approaches consider how users interact with web pages, they do not relate AOIs with the underlying source code. We propose a novel approach that integrates web page and eye tracking data driven approaches for automatic AOI detection. This approach segments an entire web page into its AOIs by considering users’ interactions and relates AOIs with the underlying source code. Based on the Adjusted Rand Index measure, our approach provides the most similar segmentation to the ground-truth segmentation compared to its individual components.
- Hamed Ahmadi and Jun Kong. 2008. Efficient web browsing on small screens. In Proceedings of the Working Conference on Advanced Visual Interfaces (AVI’08). ACM, New York, NY, 23--30. DOI:https://doi.org/10.1145/1385569.1385576Google Scholar
Digital Library
- M. Elgin Akpınar and Yeliz Yesilada. 2013. Vision-based Page Segmentation Algorithm: Extended and Perceived Success. Springer International Publishing, Cham, 238--252.Google Scholar
- Chieko Asakawa and Hironobu Takagi. 2000. Annotation-based transcoding for nonvisual web access. In Proceedings of the 4th International ACM Conference on Assistive Technologies (Assets’00). ACM, New York, NY, 172--179. DOI:https://doi.org/10.1145/354324.354588Google Scholar
Digital Library
- Lidong Bing, Rui Guo, Wai Lam, Zheng-Yu Niu, and Haifeng Wang. 2014. Web page segmentation with structured prediction and its application in web page classification. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14). ACM, New York, NY, 767--776.Google Scholar
Digital Library
- Paolo Boi, Gianni Fenu, Lucio Davide Spano, and Valentino Vargiu. 2016. Reconstructing user’s attention on the web through mouse movements and perception-based content identification. ACM Trans. Appl. Percept. 13, 3, Article 15 (May 2016), 21 pages. DOI:https://doi.org/10.1145/2912124Google Scholar
Digital Library
- Georg Buscher, Edward Cutrell, and Meredith Ringel Morris. 2009. What do you see when you’re surfing?: Using eye tracking to predict salient regions of web pages. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’09). ACM, New York, NY, 21--30. DOI:https://doi.org/10.1145/1518701.1518705Google Scholar
Digital Library
- Deng Cai, Shipeng Yu, Ji-Rong Wen, and Wei-Ying Ma. 2003. VIPS: A Vision-based Page Segmentation Algorithm. Technical Report. Microsoft. 28 pages. Retrieved from https://www.microsoft.com/en-us/research/publication/vips-a-vision-based-page-segmentation-algorithm/.Google Scholar
- Ricardo J. G. B. Campello, Davoud Moulavi, and Joerg Sander. 2013. Density-based Clustering Based on Hierarchical Density Estimates. Springer, Berlin, 160--172.Google Scholar
- Joyram Chakraborty and Michael P. McGuire. 2016. Directional scan path characterization of eye tracking sequences: A multi-scale approach. In Proceedings of the Future Technologies Conference (FTC’16). 51--61. DOI:https://doi.org/10.1109/FTC.2016.7821589Google Scholar
- Dorin Comaniciu and Peter Meer. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 5 (May 2002), 603--619.Google Scholar
Digital Library
- Tom N. Cornsweet. 1956. Determination of the stimuli for involuntary drifts and saccadic eye movements. J. Optical Soc. Amer. 46, 11 (1956), 987--988.Google Scholar
Cross Ref
- Gautier Drusch and J. M. Christian Bastien. 2012. Analyzing visual scanpaths on the web using the mean shift procedure and t-pattern detection: A bottom-up approach. In Proceedings of the Conference on Ergonomie Et Interaction Homme-machine (Ergo’IHM’12). ACM, New York, NY.Google Scholar
- Sukru Eraslan and Yeliz Yesilada. 2015. Patterns in eyetracking scanpaths and the affecting factors. J. Web Eng. 14, 5--6 (Nov. 2015), 363--385.Google Scholar
- Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2014. Identifying patterns in eyetracking scanpaths in terms of visual elements of web pages. In Web Engineering, Sven Casteleyn, Gustavo Rossi, and Marco Winckler (Eds.). Springer International Publishing, Cham, 163--180.Google Scholar
- Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2016. Eye tracking scanpath analysis on web pages: How many users?. In Proceedings of the 9th Biennial ACM Symposium on Eye Tracking Research and Applications (ETRA’16). ACM, New York, NY, 103--110. DOI:https://doi.org/10.1145/2857491.2857519Google Scholar
Digital Library
- Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2016. Scanpath trend analysis on web pages: Clustering eye tracking scanpaths. ACM Trans. Web 10, 4 (Nov. 2016).Google Scholar
Digital Library
- Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2017. Less users more confidence: How AOIs don’t affect scanpath trend analysis. J. Eye Move. Res. 10, 4 (Nov. 2017). DOI:https://doi.org/10.16910/jemr.10.4.6Google Scholar
- Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD’96). AAAI Press, 226--231.Google Scholar
Digital Library
- Hanyang Feng, Wenzhe Zhang, Hesheng Wu, and Chong-Jun Wang. 2016. Web page segmentation and its application for web information crawling. In Proceedings of the IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI’16). 598--605.Google Scholar
Cross Ref
- Brendan J. Frey and Delbert Dueck. 2007. Clustering by passing messages between data points. Science 315 (2007), 972--976.Google Scholar
Cross Ref
- Xiao-Dong Gu, Jinlin Chen, Wei-Ying Ma, and Guo-Liang Chen. 2002. Visual-based content understanding toward web adaptation. In Adaptive Hypermedia and Adaptive Web-based Systems, Paul De Bra, Peter Brusilovsky, and Ricardo Conejo (Eds.). Springer, Berlin, 164--173.Google Scholar
- Tz-Huan Huang, Kai-Yin Cheng, and Yung-Yu Chuang. 2009. A collaborative benchmark for region of interest detection algorithms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 296--303.Google Scholar
Cross Ref
- Lawrence Hubert and Phipps Arabie. 1985. Comparing partitions. J. Classif. 2, 1 (1 Dec 1985), 193--218. DOI:https://doi.org/10.1007/BF01908075Google Scholar
Cross Ref
- Laurent Itti. 2005. Models of bottom-up attention and saliency. In Neurobiology of Attention, Laurent Itti, Geraint Rees, and John K. Tsotsos (Eds.). Academic Press, Burlington, 576--582. DOI:https://doi.org/10.1016/B978-012375731-9/50098-7Google Scholar
- Jeff Johnson. 2010. Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Rules. Morgan Kaufmann Publishers Inc., San Francisco, CA.Google Scholar
- Christian Kohlschütter, Peter Fankhauser, and Wolfgang Nejdl. 2010. Boilerplate detection using shallow text features. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (WSDM’10). ACM, New York, NY, 441--450. DOI:https://doi.org/10.1145/1718487.1718542Google Scholar
Digital Library
- Christian Kohlschütter and Wolfgang Nejdl. 2008. A densitometric approach to web page segmentation. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM’08). ACM, New York, NY, 1173--1182. DOI:https://doi.org/10.1145/1458082.1458237Google Scholar
Digital Library
- Robert Kreuzer, Jurriaan Hage, and Ad Feelders. 2015. A Quantitative Comparison of Semantic Web Page Segmentation Approaches. Springer International Publishing, Cham, 374--391. DOI:https://doi.org/10.1007/978-3-319-19890-3_24Google Scholar
- Aoqi Li, Yingxue Zhang, and Zhenzhong Chen. 2017. Scanpath mining of eye movement trajectories for visual attention analysis. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME). 535--540. DOI:https://doi.org/10.1109/ICME.2017.8019507Google Scholar
Cross Ref
- James B. MacQueen. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics. University of California Press, Berkeley, CA, 281--297. https://projecteuclid.org/euclid.bsmsp/1200512992.Google Scholar
- Florian Marchal, Sylvain Castagnos, and Anne Boyer. 2016. Tell me what you see, I will tell you what you remember. In Proceedings of the Conference on User Modeling Adaptation and Personalization (UMAP’16). ACM, New York, NY, 293--294.Google Scholar
Digital Library
- Leland McInnes, John Healy, and Steve Astels. No date. Comparing Python Clustering Algorithms. Retrieved from http://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html.Google Scholar
- Eleni Michailidou, Simon Harper, and Sean Bechhofer. 2008. Visual complexity and aesthetic perception of web pages. In Proceedings of the 26th Annual ACM International Conference on Design of Communication (SIGDOC’08). ACM, New York, NY, 215--224. DOI:https://doi.org/10.1145/1456536.1456581Google Scholar
Digital Library
- Hadi Mohammadzadeh, Thomas Gottron, Franz Schweiggert, and Gholamreza Nakhaeizadeh. 2013. Extracting the Main Content of Web Documents Based on Character Encoding and a Naive Smoothing Method. Springer, Berlin, 217--236. DOI:https://doi.org/10.1007/978-3-642-36177-7_14Google Scholar
- Anthony Nguyen, Vinod Chandran, and Sridha Sridharan. 2006. Gaze tracking for region of interest coding in JPEG 2000. Signal Process.: Image Commun. 21, 5 (2006), 359--377.Google Scholar
Cross Ref
- Jacob Nielson. 2000. Why You Only Need to Test with 5 Users. Retrieved from https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/.Google Scholar
- Pontus Olsson. 2007. Real-time and offline filters for eye tracking. Master’s thesis. KTH Royal Institute of Technology.Google Scholar
- Bing Pan, Helene A. Hembrooke, Geri K. Gay, Laura A. Granka, Matthew K. Feusner, and Jill K. Newman. 2004. The determinants of web page viewing behavior: An eye-tracking study. In Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA’04). ACM, New York, NY, 147--154. DOI:https://doi.org/10.1145/968363.968391Google Scholar
- Kara Pernice and Jakob Nielsen. 2009. How to Conduct Eyetracking Studies. Technical Report. Nielsen Norman Group. Retrieved from https://media.nngroup.com/media/reports/free/How_to_Conduct_Eyetracking_Studies.pdf.Google Scholar
- Katharina Reinecke, Tom Yeh, Luke Miratrix, Rahmatri Mardiko, Yuechen Zhao, Jenny Liu, and Krzysztof Z. Gajos. 2013. Predicting users’ first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’13). ACM, New York, NY, 2049--2058. DOI:https://doi.org/10.1145/2470654.2481281Google Scholar
- Andrés Sanoja and Stéphane Gançarski. 2014. Block-o-matic: A web page segmentation framework. In Proceedings of the International Conference on Multimedia Computing and Systems (ICMCS’14). 595--600.Google Scholar
Cross Ref
- Anthony Santella and Doug DeCarlo. 2004. Robust clustering of eye movement recordings for quantification of visual interest. In Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA’04). ACM, New York, NY, 27--34.Google Scholar
Digital Library
- scikit-learn developers. [n.d.]. Clustering. Retrieved from http://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering.Google Scholar
- scikit-learn developers. [n.d.]. sklearn.cluster.AgglomerativeClustering. Retrieved from http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering.Google Scholar
- Lisa M. Sullivan, Janice Weinberg, and John F. Keaney. 2016. Common statistical pitfalls in basic science research. J. Amer. Heart Assoc. 5, 10 (2016), e004142.Google Scholar
Cross Ref
- Tobii Technology AB. 2010. Tobii Studio 2.X User Manual (Sep. 2010). Tobii Technology AB.Google Scholar
- Geoffrey Underwood, Tom Foulsham, and Katherine Humphrey. 2009. Saliency and scan patterns in the inspection of real-world scenes: Eye movements during encoding and recognition. Vis. Cogn. 17, 6--7 (2009), 812--834. DOI:https://doi.org/10.1080/13506280902771278Google Scholar
Cross Ref
- Jessica M. Utts. 2014. Seeing Through Statistics (4th ed.). Cengage Learning, Stamford, CT.Google Scholar
- Ou Wu, Yunfei Chen, Bing Li, and Weiming Hu. 2011. Evaluating the visual quality of web pages using a computational aesthetic approach. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM’11). ACM, New York, NY, 337--346. DOI:https://doi.org/10.1145/1935826.1935883Google Scholar
Digital Library
- Xin Yang and Yuanchun Shi. 2009. Enhanced gestalt theory guided web page segmentation for mobile browsing. In Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Vol. 3. 46--49. DOI:https://doi.org/10.1109/WI-IAT.2009.227Google Scholar
Digital Library
- Yeliz Yesilada. 2011. Web Page Segmentation: A Review. Technical Report. Middle East Technical University. Retrieved from http://emine.ncc.metu.edu.tr/deliverables/emine_D0.pdf.Google Scholar
- Yeliz Yesilada, Simon Harper, and Sukru Eraslan. 2013. Experiential transcoding: An EyeTracking approach. In Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility (W4A’13). ACM, New York, NY.Google Scholar
Digital Library
- Yeliz Yesilada, Caroline Jay, Robert Stevens, and Simon Harper. 2008. Validating the use and role of visual elements of web pages in navigation with an eye-tracking study. In Proceedings of the 17th International Conference on World Wide Web (WWW’08). ACM, New York, NY, 11--20. DOI:https://doi.org/10.1145/1367497.1367500Google Scholar
Digital Library
- Xinyi Yin and Wee Sun Lee. 2004. Using link analysis to improve layout on mobile devices. In Proceedings of the 13th International Conference on World Wide Web (WWW’04). ACM, New York, NY, 338--344. DOI:https://doi.org/10.1145/988672.988718Google Scholar
Digital Library
- Yu Chen, Xing Xie, Wei-Ying Ma, and Hong-Jiang Zhang. 2005. Adapting web pages for small-screen devices. IEEE Internet Comput. 9, 1 (Jan 2005), 50--56. DOI:https://doi.org/10.1109/MIC.2005.5Google Scholar
- Jan Zeleny, Radek Burget, and Jaroslav Zendulka. 2017. Box clustering segmentation: A new method for vision-based web page preprocessing. Info. Process. Manage. 53, 3 (2017), 735--750.Google Scholar
Digital Library
Index Terms
“The Best of Both Worlds!”: Integration of Web Page and Eye Tracking Data Driven Approaches for Automatic AOI Detection
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