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Measuring the Visual Complexities of Web Pages

Published:01 March 2013Publication History
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Abstract

Visual complexities (VisComs) of Web pages significantly affect user experience, and automatic evaluation can facilitate a large number of Web-based applications. The construction of a model for measuring the VisComs of Web pages requires the extraction of typical features and learning based on labeled Web pages. However, as far as the authors are aware, little headway has been made on measuring VisCom in Web mining and machine learning. The present article provides a new approach combining Web mining techniques and machine learning algorithms for measuring the VisComs of Web pages. The structure of a Web page is first analyzed, and the layout is then extracted. Using a Web page as a semistructured image, three classes of features are extracted to construct a feature vector. The feature vector is fed into a learned measuring function to calculate the VisCom of the page.

In the proposed approach of the present study, the type of the measuring function and its learning depend on the quantification strategy for VisCom. Aside from using a category and a score to represent VisCom as existing work, this study presents a new strategy utilizing a distribution to quantify the VisCom of a Web page. Empirical evaluation suggests the effectiveness of the proposed approach in terms of both features and learning algorithms.

References

  1. Ahmad, A. -R., Basir, O., Hassanein, K., and Azam, S. 2008. An intelligent expert systems approach to layout decision analysis and design under uncertainty. Stud. Comput. Intell. 97, 321--364.Google ScholarGoogle Scholar
  2. Amazon. 2005. Amazon’s mechanical turk. https://www.mturk.com/mturk/welcome.Google ScholarGoogle Scholar
  3. Annett, J. 2002. Subjective rating scales: Science or art? Ergonomics 45, 14, 966--987.Google ScholarGoogle ScholarCross RefCross Ref
  4. Berlyne, D. 1974. Studies in the New Experimental Aesthetics. Hemi-sphere Publishing.Google ScholarGoogle Scholar
  5. Breiman, L. 2001. Random forests. Mach. Learn. 45, 1, 5--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Cai, D., Yu, S., Wen, J. -R., and Ma, W. -Y. 2003a. Extracting content structure for web pages based on visual representation. In Proceedings of the 5th Asia-Pacific Web Conference on Web Technologies and Applications. 406--417. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Cai, D., Yu, S., Wen, J. -R., and Ma, W.-Y. 2003b. Vips: A vision-based page segmentation algorithm. Tech. rep. MSR-TR-2003-79. Microsoft.Google ScholarGoogle Scholar
  8. Cao, L. J., Chua , K. S., and Chong, W. K. 2003. A comparison of pca, kpca and ica for dimensionality reduction in support vector machine. Neurocomput. 55, 1--2, 321--336.Google ScholarGoogle ScholarCross RefCross Ref
  9. Chen, G. and Choi, B. 2008. Web page genre classification. In Proceedings of the ACM Symposium on Applied Computing. 2353--2357. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Cheng, H. and Cant-Paz, E. 2010. Personalized click prediction in sponsored search. In Proceedings of the ACM International Conference on Web Search and Data Mining. 351--360. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. CV. 2013. http://en.wikipedia.org/wiki/cross-validation statistics.Google ScholarGoogle Scholar
  12. Datta, R., Joshi, D., Li , J., and Wang, J. Z. 2006. Studying aesthetics in photographic images using a computational approach. In Proceedings of the European Conference on Computer Vision. 288--301. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Donderi, D. C. 2006. Visual complexity: A review. Psychol. Bull. 132, 1, 73--97.Google ScholarGoogle ScholarCross RefCross Ref
  14. Duda, R. O., Hart, P. E., and Stork, D. G. 2001. Pattern Classification 2nd Ed. John Wiley & Sons. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Dwork, C., Kumar, R., Naor, M., and Sivakumarc, D. 2001. Rank aggregation methods for the web. In Proceedings of the 10th International Conference on World Wide Web. ACM, 613--622. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Fawcett, T. 2006. An introduction to roc analysis. Pattern Recogn. Lett. 27, 861--874. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Fleiss, J. L. 1971. Measuring nominal scale agreement among many raters. Psychol. Bull. 76, 5, 378--382.Google ScholarGoogle ScholarCross RefCross Ref
  18. Forsythe, A. 2009. Visual complexity: Is that all there is? In Proceedings of the 13th International Conference on Human-Computer Interaction. Lecture Notes in Artificial Intelligence, vol. 5639, Springer, 158--166. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Forsythe, A., Sheehy, N., and Sawey, M. 2003. Measuring icon complexity: An automated analysis. Behav. Res. Methods Instrum. Comput. 32, 2, 334--342.Google ScholarGoogle ScholarCross RefCross Ref
  20. Franc, V. and Sonnenburg, S. 2008. Optimized cutting plane algorithm for support vector machines. In Proceedings of the International Conference on Machine Learning. 320--327. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Geissler, G. L., Zinkhan, G. M., and Watson, R. T. 2006. The influence of home page complexity on consumer attention, attitudes, and purchase intent. J. Advertising 35, 2, 69--80.Google ScholarGoogle ScholarCross RefCross Ref
  22. Gero, J. S. and Kazakov, V. 2004. On measuring the visual complexity of 3d objects. J. Des. Sci. Technol. 12, 1, 35--44.Google ScholarGoogle Scholar
  23. Geusebroek, J. and Smeulders, A. 2005. A six-stimulus theory for stochastic texture. Int. J. Comput. Vision 62, 1--2, 7--16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Harper, S., Michailidou, E., and Stevens, R. 2009. Toward a definition of visual complexity as an implicit measure of cognitive load. ACM Trans. Appl. Percept. 6, 2, Artical 10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Hasler, S. and Susstrunk, S. 2003. Measuring colorfulness in real images. Proc. SPIE Electron. Imag:Hum. Vision Electron. 87--95.Google ScholarGoogle Scholar
  26. Jiang, D., Pei, J., and Li, H. 2010. Web search/browse log mining: Challenges, methods, and applications. In Proceedings of the International World Wide Web Conference. 1351--1352. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Kim, J. and Wilhelm, T. 2008. What is a complex graph? Phys. A 387, 2637--2652.Google ScholarGoogle ScholarCross RefCross Ref
  28. Kohlschtter, C. and Nejdl, W. 2008. A densitometric approach to web page segmentation. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). 1173--1182. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Lam, F. C. and Longnecker, M. T. 1983. A modified wilconxon rank sum test for paired data. Biometrika 70, 510--513.Google ScholarGoogle ScholarCross RefCross Ref
  30. Levering, R. and Cutler, M. 2009. Cost-Sensitive feature extraction and selection in genre classification. J. Lang. Technol. Comput. Linguistics 24, 2, 57--72.Google ScholarGoogle Scholar
  31. Liu, B. 2007. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Michailidou, E. 2009. Visual complexity rankings and accessibility metrics. Ph.D. thesis, University of Manchester.Google ScholarGoogle Scholar
  33. Michailidou, E., Harper, S., and Bechhofer, S. 2008. Visual complexity and aesthetic perception of web pages. In Proceedings of the ACM International Conference on Design of Communication (SIGDOC). 215--223. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Mitchell, T. M. 1997. Machine Learning. McGraw Hill. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Ninassi, A., Meur, O. L., Olivier, P. L., and Barba, D. 2009. Considering temporal variations of spatial visual distortions in video quality assessment. IEEE J. Sel. Top. Sign. Proces. 3, 2, 253--265.Google ScholarGoogle ScholarCross RefCross Ref
  36. Pandir, M. and Knight, J. 2006. Homepage aesthetics: The search for preference factors and the challenges of subjectivity. Interact. Comput. 18, 6, 1351--1370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Papachristos, E., Tselios, N., and Avouris, T. 2006. Bayesian modeling of impact of colour on web credibility. In Proceedings of the European Conference on Artificial Intelligence. 41--45. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Park, S., Choi, D., and Kim, J. 2004. Critical factors for the aesthetic fidelity of web pages: Empirical studies with professional web designers and users. Interact. Comput. 16, 351--376.Google ScholarGoogle Scholar
  39. Pedro, J. S. and Siersdorfer, S. 2009. Ranking and classifying attractiveness of photos in folksonomies. In Proceedings of the International World Wide Web Conference. 771--780. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Pieters, R., Wedel, M., and Batra, R. 2010. The stopping power of advertising: Measures and effects of visual complexity. J. Market. 74, 5, 48--60.Google ScholarGoogle ScholarCross RefCross Ref
  41. Pitler, E. and Nenkova, A. 2008. Revisiting readability: A unified framework for predicting text quality. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). 186--195. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Rosenholtz, R., Li, Y., and Nakano, L. 2007. Measuring visual clutter. J. Vision 7, 2, 1--22.Google ScholarGoogle ScholarCross RefCross Ref
  43. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. 1986. Learning representations by back-propagating errors. Nature 323, 6088, 533--536.Google ScholarGoogle Scholar
  44. Schaik, R. and Ling, J. 1991. The effects of screen ratio and order on information retrieval in web pages. IEEE Trans. Syst. Man Cybern. 21, 3, 660--674.Google ScholarGoogle Scholar
  45. Song, G. 2007. Analysis of web page complexity through visual segmentation. In Proceedings of the 12th International Conference on Human-Computer Interaction. Lecture Notes in Artificial Intelligence, vol. 4553, Springer, 114--123. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Song, R., Liu, H., Wen, J. -R., and Ma, W. -Y. 2004. Learning block importance models for web pages. In Proceedings of the International World Wide Web Conference. 203--211. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Sickel, C., Ebner, M., and Holzinger, A. 2010. The xaos metric - Understanding visual complexity as measure of usability. In Proceedings of the 6th Symposium of the Workgroup HCI & UE of the Austrian Computer Society (USAB’’10). 278--290. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Thomas, C. and Tullis, S. 1998. A method for evaluating web page design concepts. In Proceedings of the International Conference on Human Factors in Computing Systems (CHI’98). 323--324. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Tsochantaridis, I., Hofmann, T., Joachims, T., and Altun, Y. 2004. Support vector machine learning for interdependent and structured output spaces. In Proceedings of the International Conference on Machine Learning. 104--112. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Tuch, A. N., Bargas-Avila, J., Opwis, K., and Wilhem, F. 2009. Visual complexity of websites: Effects on users’ experience, physiology, performance, and memory. Int. J. Hum. Comput. Stud. 67, 703--715. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Tuch, A. N., Kreibig, S., Roth, S., Bargas-Avila, J., Opwis, K., and Wilhem, F. H. 2011. The role of visual complexity in affective reactions to web pages: Subjective, eye movement, and cardiovascular responses. IEEE Trans. Affective Comput. 2, 4, 230--236. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Vapnik, V. 1998. Statistical Learning Theory. Wiley.Google ScholarGoogle Scholar
  53. Wang, M. and Hua, X.-S. 2011. Active learning in multimedia annotation and retrieval: A survey. ACM Trans. Intell. Syst. Technol. 2, 2, Article 10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Wu, O., Chen, Y., Li, B., and Hu, W. 2011. Evaluating the visual quality of web pages using a computational aesthetics approach. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM). 337--346. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Zadeh, L. A. 1965. Fuzzy sets. Inf. Control 8, 338--353.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Zheng, X. S., Chakraborty, I., Lin, J. J. -W., and Rauschenberger, R. 2008. Developing quantitative metrics to predict users’ perceptions of interface design. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (HFES). 2023--2027.Google ScholarGoogle Scholar
  57. Zheng, X. S., Chakraborty, I., Lin, J. J. -W., and Rauschenberger, R. 2009. Correlating low-level image statistics with users- rapid aesthetic and affective judgments of web pages. In Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI). 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Transactions on the Web
      ACM Transactions on the Web  Volume 7, Issue 1
      March 2013
      128 pages
      ISSN:1559-1131
      EISSN:1559-114X
      DOI:10.1145/2435215
      Issue’s Table of Contents

      Copyright © 2013 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 March 2013
      • Revised: 1 October 2012
      • Accepted: 1 October 2012
      • Received: 1 February 2011
      Published in tweb Volume 7, Issue 1

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