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Automated construction of web accessibility models from transaction click-streams

Published: 20 April 2009 Publication History

Abstract

Screen readers, the dominant assistive technology used by visually impaired people to access the Web, function by speaking out the content of the screen serially. Using screen readers for conducting online transactions can cause considerable information overload, because transactions, such as shopping and paying bills, typically involve a number of steps spanning several web pages. One can combat this overload by using a transaction model for web accessibility that presents only fragments of web pages that are needed for doing transactions. We can realize such a model by coupling a process automaton, encoding states of a transaction, with concept classifiers that identify page fragments "relevant" to a particular state of the transaction. In this paper we present a fully automated process that synergistically combines several techniques for transforming unlabeled click-stream data generated by transactions into a transactionmodel. These techniques include web content analysis to partition a web page into segments consisting of semantically related content, contextual analysis of data surrounding clickable objects in a page, and machine learning methods, such as clustering of page segments based on contextual analysis, statistical classification, and automata learning. The use of unlabeled click streams in building transaction models has important benefits: (i) visually impaired users do not have to depend on sighted users for creating manually labeled training data to construct the models; (ii) it is possible to mine personalized models from unlabeled transaction click-streams associated with sites that visually impaired users visit regularly; (iii) since unlabeled data is relatively easy to obtain, it is feasible to scale up the construction of domain-specific transaction models (e.g., separate models for shopping, airline reservations, bill payments, etc.); (iv) adjusting the performance of deployed models over timtime with new training data is also doable. We provide preliminary experimental evidence of the practical effectiveness of both domain-specific, as well as personalized accessibility transaction models built using our approach. Finally, this approach is applicable for building transaction models for mobile devices with limited-size displays, as well as for creating wrappers for information extraction from web sites.

References

[1]
JAWS Screen Reader. http://www.freedomscientific.com.
[2]
J. Allan, editor. Topic Detection and Tracking: Event-based Information Organization. Kluwer Academic Publishers, 2002.
[3]
J. F. Allen, N. Chambers, G. Ferguson, L. Galescu, H. Jung,M. D. Swift, and W. Taysom. Plow: A collaborative task learning agent. In Proc. of AAAI, 2007.
[4]
E. Amitay and C. Paris. Automatically summarising web sites -- is there a way around it? In Proc. of the CIKM, 2000.
[5]
C. Asakawa and T. Itoh. User interface of a home page reader. In Proc. of ASSETS, 1998.
[6]
A. Banerjee and J. Ghosh. Click-stream clustering using weighted longest common subsequences. In Proc. of the Web Mining Workshop at the 1st SIAM Conference on Data Mining, pages 33--40, 2001.
[7]
H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware query suggestion by mining click-through and session data. In Proc. of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 875--883, 2008.
[8]
V. Crescenzi and G. Mecca. Automatic information extraction from large websites. Journal of ACM, 51(5):731--779, 2004.
[9]
A. Cypher. Watch what i do: Programming by demonstration. MIT Press, 1993.
[10]
X. Dong, A. Halevy, J. Madhavan, E. Nemes, and J. Zhang. Similarity search for web services. In VLDB '04: Proc. of the Thirtieth international conference on Very large data bases, pages 372--383, 2004.
[11]
D. Geoffray. The internet through the eyes of windows-eyes. In Proc. of Tech. and Persons with Disabilities Conf., 1999.
[12]
G. Greco, A. Guzzo, L. Pontieri, and D. Sacca. Mining expressive process models by clustering workflow traces. 2004.
[13]
J. E. Hopcroft and J. D. Ullman. Introduction to Automata Theory, Languages, and Computation. Addison-Wesley, 1979.
[14]
B. B.-M. J.-Y. Delort and M. Rifqi. Enhanced web document summarization using hyperlinks. In Proceedings of the fourteenth ACM conference on Hypertext and hypermedia, pages 208--215, 2003.
[15]
P. Jaccard. Bulletin del la soci?t? vaudoisedes sciences naturelles 37. pages 241--272, 1901.
[16]
T. Lau. Programming by Demonstration: a Machine Learning Approach. PhD thesis, University of Washington, 2001.
[17]
J. Lee, M. Podlaseck, E. Schonberg, and R. Hoch. Visualization and analysis of click-stream data of online stores for understanding web merchandising. Data Min. Knowl. Discov., 5(1--2):59--84, 2001.
[18]
K. Lerman, A. Plangprasopchok, and C. A. Knoblock. Automatically labeling the inputs and outputs of web services. In Proc. of AAAI, 2006.
[19]
G. Leshed, E. M. Haber, T. Matthews, and T. Lau. Coscripter: automating & sharing how-to knowledge in the enterprise. In Proc. of CHI, 2008.
[20]
J. Mahmud, Y. Borodin, and I. V. Ramakrishnan. Csurf: A context-directed non-visual web-browser. In Proc. of the WWW, 2007.
[21]
J. Mahmud, Y. Borodin, and I. V. Ramakrishnan. Assistive browser for conducting web transactions. In Proc. of the IUI, 2008.
[22]
S. Mukherjee, G. Yang, W. Tan, and I. Ramakrishnan. Automatic discovery of semantic structures in html documents. In Proc. of ICDAR, 2003.
[23]
K. Murphy. Passively learning finite automata, 1996.
[24]
O. Nasraoui, C. Cardona, and C. Rojas. Mining of evolving web click-streams with explicit retrieval similarity measures. In Proc. of "International Web Dynamics Workshop", International World Wide Web Conference, 2004.
[25]
M. T. Ozsu. A web page prediction model based on click-stream tree representation of user behavior. In Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 535--540, 2003.
[26]
M. F. Porter. An algorithm for suffix stripping. pages 313--316, 1997.
[27]
R. Silva, J. Zhang, and J. Shanahan. Probabilistic workflow mining. 2005.
[28]
R. Silva, J. Zhang, and J. G. Shanahan. Probabilistic workflow mining. In Proc. of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 275--284, 2005.
[29]
A. Strehl. Relationship-based Clustering and Cluster Ensembles for High-dimensional Data Mining. PhD thesis, The University of Texas at Austin, May 2002.
[30]
Z. Sun, J. Mahmud, S. Mukherjee, and I. V. Ramakrishnan. Model-directed web transactions under constrained modalities. In Proc. of WWW, 2006.
[31]
R. Tuchinda, P. Szekely, and C. A. Knoblock. Building data integration queries by demonstration. In Proc. of the IUI, 2007.
[32]
W. van der Aalst and A.Weijters. Process mining: A research agenda. Computers and Industry, 53:231--244, 2004.
[33]
V. Vapnik. Principles of risk minimization for learning theory. In D. S. Lippman, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3, 1992.
[34]
S. Yu, D. Cai, J.-R. Wen, and W.-Y. Ma. Improving pseudo-relevance feedback in web information retrieval using web page segnmentation. In Proc. of Intl. World Wide Web Conf. (WWW), 2003.
[35]
M. Zajicek, C. Powell, and C. Reeves. Web search and orientation with brookestalk. In Proc. of Tech. and Persons with Disabilities Conf., 1999.

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cover image ACM Conferences
WWW '09: Proceedings of the 18th international conference on World wide web
April 2009
1280 pages
ISBN:9781605584874
DOI:10.1145/1526709

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Association for Computing Machinery

New York, NY, United States

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Published: 20 April 2009

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Author Tags

  1. context
  2. machine learning
  3. process models
  4. web transaction

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Cited By

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  • (2021)An examination on ‘website accessibility’ for active engagement of visually impaired e-commerce customersTechnology and Disability10.3233/TAD-200293(1-7)Online publication date: 11-Jun-2021
  • (2021)Towards Web Browsing Assistance Using Task Modeling Based on Observed UsagesKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-030-66196-0_21(453-471)Online publication date: 14-Jan-2021
  • (2019)Auto-Suggesting Browsing Actions for Personalized Web Screen ReadingProceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3320435.3320460(252-260)Online publication date: 7-Jun-2019
  • (2017)Non-visual Web Browsing: Beyond Web AccessibilityUniversal Access in Human–Computer Interaction. Designing Novel Interactions10.1007/978-3-319-58703-5_24(322-334)Online publication date: 16-May-2017
  • (2014)Forms2Dialog: Automatic dialog generation for Web tasks2014 IEEE Spoken Language Technology Workshop (SLT)10.1109/SLT.2014.7078643(608-613)Online publication date: Dec-2014
  • (2013)LiveActionACM Transactions on Interactive Intelligent Systems10.1145/2533670.25336723:3(1-23)Online publication date: 1-Oct-2013
  • (2013)Predictive web automation assistant for people with vision impairmentsProceedings of the 22nd international conference on World Wide Web10.1145/2488388.2488478(1031-1040)Online publication date: 13-May-2013
  • (2012)Crowd-based recognition of web interaction patternsAdjunct proceedings of the 25th annual ACM symposium on User interface software and technology10.1145/2380296.2380341(99-100)Online publication date: 7-Oct-2012
  • (2012)An intuitive accessible web automation user interfaceProceedings of the International Cross-Disciplinary Conference on Web Accessibility10.1145/2207016.2207054(1-4)Online publication date: 16-Apr-2012
  • (2012)Transaction models for Web accessibilityWorld Wide Web10.1007/s11280-011-0135-315:4(383-408)Online publication date: 1-Jul-2012

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