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Arbelaitz Olatz (University of the Basque Country, Spain), Lojo Aizea (University of the Basque Country, Spain), Muguerza Javier (University of the Basque Country, Spain), Perona Iñigo (University of the Basque Country, Spain)
Global versus modular link prediction approach for discapnet: website focused to visually impaired people
Annals of Computer Science and Information Systems, 2014, vol. 2, s. 51-58, rys., tab., bibliogr. 21 poz.
Słowa kluczowe
Osoby niepełnosprawne, Użytkownicy internetu, Internet
Disabled people, Internet users, Internet
Web personalization becomes essential in industries and specially for the case of users with special needs such as visually impaired people. Adaptation may very much speed up the navigation of visually impaired people and contribute to diminish the existing technological gap. This work is the first stage of a web mining process carried out in discapnet: a website created to promote the social and work integration of people with disabilities where slow navigation has been detected. Based on observation in-use where behaviours emerge applying a web mining process to server log data, we designed a system to generate user navigation profiles and adapt to the web site through link prediction. Two approaches for user profiling were implemented: a global system built based on the complete database and a modular approach carried out discovering the navigation profiles within different zones. Although both approaches are effective, the modular approach outperforms. When 25\% of the navigation of the new user has happened the designed system is able to propose a set of links where nearly 60\% of them (2 out of 3) is among the ones the new user will be using in the future. This will definitely make the navigation easier saving a lot of time.(original abstract)
Pełny tekst
  1. "Common log format (clf)" 1995. The World Wide Web Consortium (W3C). Available:, accessed 04-05-2014.
  2. "EvalAccess: Web Service tool for evaluating web accessibility". Available:, accessed 04-05-2014.
  3. "Fundaci ´on ONCE for cooperation and social inclusion of people with disabilities". Available:, accessed 04-05-2014.
  4. "WAI Guidelines and Techniques". Available:, accessed 04-05-2014.
  5. Apaolaza A., Harper S., Jay C., 2013."Understanding Users in the Wild". Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility, pp.1-4,
  6. Arbelaitz O., Gurrutxaga I., Lojo A., Muguerza J., P ´erez J., Perona I., 2012. "Adaptation of the user navigation scheme using clustering and frequent pattern mining techniques for profiling". 4th International Conference on Knowledge Discovery and Information Retrieval (KDIR), pp.187-192.
  7. Brusilovsky P., Kobsa A., Nejdl W., 2007. "The Adaptive Web: Methods and Strategies of Web Personalization". Lecture Notes in Computer Science (Springer), Berlin, .
  8. Chaffey D., Ellis-Chadwick F., Johnston K., Mayer R., 2006. "Internet Marketing". Prentice Hall/Financial Times.
  9. Chandrashekar S., Stockman T., Fels D., Benedyk R., 2006. "Using Think Aloud Protocol with Blind Users: A Case for Inclusive Usability Evaluation Methods". Proceedings of the 8th International ACM SIGACCESS Conference on Computers and Accessibility, pp.251-252,
  10. Chordia B., Adhiya K., 2011. "Grouping web access sequences using sequence alignment method". Indian Journal of Computer Science and Engineering (IJCSE), vol. 2, pp.308-314.
  11. Craven J., Brophy P., 2013. "Nonvisual access to the digital library: The use of digital library interfaces by blind and visually impaired people". In Technical report No. 145. Manchester, United Kingdom: Centre for Research in Library and Information Management.
  12. Dasarathy S., 1991. "Nearest neighbor (NN) norms : NN pattern classification techniques". IEEE Computer Society Press.
  13. Garcia E., Romero C., Ventura S., Castro C.D., 2009." An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering". User Modeling and User-Adapted Interaction, vol. 19, pp.99-132,
  14. Gusfield D., 1997 "Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology". Cambridge University Press, New York, NY, USA, .
  15. He D., Gker A., 2000. "Detecting session boundaries from web user logs". In Proceedings of of the BCS-IRSG 22nd Annual Colloquium on Information Retrieval Research, pp.57-66.
  16. Kaufman L., Rousseeuw P.J., 1990. "Finding Groups in Data: An Introduction to Cluster Analysis". Wiley-Interscience, .
  17. Liu L., ¨Ozsu M.T., 2009. "Encyclopedia of Database Systems. In: PAM (Partitioning Around Medoids)."Springer US, .
  18. Pierrakos D., Paliouras G., Papatheodorou C., Spyropoulos C.D., 2003 "Web usage mining as a tool for personalization: A survey". User Modeling and User-Adapted Interaction, vol. 13, pp.311-372, .
  19. Turban E., Gehrke D., 2000. "Determinants of e-commerce website". Human Systems Management, vol. 19, pp.111-120.
  20. Vigo M., Harper S., 2013. "Challenging information foraging theory: screen reader users are not always driven by information scent". Proceedings of the 24th ACM Conference on Hypertext and Social Media, pp.60-68,
  21. Zaki M.J., 2001. "Spade: An efficient algorithm for mining frequent sequences". Machine Learning, vol. 42, pp.31-60,
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