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11 th Postgraduate Research Symposium

By: Tsoi Ho Keung Supervisor: Dr. Li Chen Co-Supervisor: Prof. Jiming Liu. 11 th Postgraduate Research Symposium. Title. Understanding the Cultural Influence on Tagging Pattern. Motivation. Cultural originality determine/affect human behavior

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11 th Postgraduate Research Symposium

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  1. By: Tsoi Ho Keung Supervisor: Dr. Li Chen Co-Supervisor: Prof. Jiming Liu 11th Postgraduate Research Symposium

  2. Title • Understanding the Cultural Influence on Tagging Pattern

  3. Motivation • Cultural originality determine/affect human behavior • Examples: greeting method, table manner, and you name it… • Cultural differences found in consumer behavior [1]. • Western countries have individualism and a low context culture • Eastern countries have collectivism and a high context culture • How about tagging behavior? We are interested in exploring whether differences exist in this area. Reference: [1] Chau, P. Y. K., Cole, M., Massey, A. P., Montoya-Weiss, M. and O'Keefe, R. M. 2002. Cultural differences in the online behavior of consumers. Communications of the ACM 45 (10), 138-143.

  4. Introduction • What is tag? • User-created annotation, in the form of keywords, short-phrases, to describe a resource. • What is the usage of assigning tags? • Search, personal management goal • Example systems • Flickr, Last.FM, DEL.icio.us, Digg…

  5. Experimental Data (requirement) • Two datasets • Different target group • Tagging-enabled • Share common domain • The following websites fulfilled our criteria • SongTaste.com • Last.FM

  6. Experimental Data (sources) • Target user  Chinese • Popular in China (2.3M registered users) • Song listening available • Let users comment • Different rankings • Tag application • Target user  European • Popular (30M registered users) • Song listening available • Let users comment • Different rankings • Tag application SongTaste Last.FM

  7. Experimental Data (Dataset) • 200 popular songs(as at 6th Dec, 09) • 6,500 users applied at least 1 tag • Avg. tags applied: 10.3 (SD 74.47) • 200 popular songs(as at 6th Dec, 09) • 6,500 users applied at least 1 tag • Avg. tags applied: 62.1 (SD 36.34) SongTaste Last.FM

  8. Research Questions • RQ1: What is the tag agreement among friends in both cultures? • RQ2: What is the tag agreement among members in both cultures? • RQ3: What is the tag non-obviousness index in oriental users compare with western user? • RQ4: How the tags classes distribution diverse from oriental users to western user?

  9. Metrics • Evaluation method from [1] as baseline measurement • t-test assuming unequal variances with a risk level(α) of 0.05 is used for comparing the datasets Reference: [1] U. Farooq, T. G. Kannampallil, Y. Song, C. H. Ganoe, J. M. Carroll, and L. Giles. Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics. In GROUP ’07: Proceedings of the 2007 international ACM conference on Supporting group work, pages 351–360, New York, NY, USA, 2007. ACM.

  10. Tag Agreement among Friends & among Members • RQ1: What is the tag agreement among friends in both cultures? • RQ2: What is the tag agreement among members in both cultures?

  11. Tag Agreement among Friends & among Members • Symmetric Jaccard Coefficient • Tuser: the set of tags user applied • Tfriend: the set of tags user’s friends applied

  12. Tag Agreement among Friends & among Members • Friends Definition • In both systems, user can explicitly state who their friends are. • Members Definition • Similarly, both systems allow users comment on a song. We define users who shared a common discussion maintain a membership

  13. Tag Agreement among Friends & among Members p< 0.05 (t=1.96) p< 0.05 (t=1.96) p< 0.05 (t=1.96) p< 0.05 (t=1.96) ۚۚt-Test: Paired Two Sample for Means, p-value less than 0.05 is significant

  14. Tag non-obviousness • RQ3: What is the tag non-obviousness index in oriental users compare with western user? • Definition: The ratio of tags not appear in the content to the total number of tags of that item • To access the usefulness of a tag

  15. Tag non-obviousness • Formally, we have to evaluate this property • (t=2.60, p = 0.004)

  16. Tag Classes Distribution • RQ4: How the tags classes distribution diverse from oriental users to western user? • Another 200 songs common in both systems are considered • Classify the tags into different categories • Two classification schemes

  17. Tag Classes Distribution • Examples of the three categories • Examples of the seven categories

  18. Tag Classes Distribution Three Categories Remarks: These are average percentage

  19. Tag Classes Distribution Seven Categories Remarks: These are average percentage

  20. Conclusion • The two cultures exhibit different tagging behavior!!!

  21. What’s next? • Bearing the different tagging patterns in mind, we can.. • Develop cultural-aware tag recommender system and; • Provide tailor-made tag recommendation based on users’ cultural originality and; • much more…

  22. Coming Soon… • Cultural-aware Semantic Map based on SOM Tag Recommender

  23. Question & Answer • Thank you 

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