Skip to main content
SpringerLink
Log in

Quality based dynamic incentive tagging

  • Published:
Distributed and Parallel Databases Aims and scope Submit manuscript

Abstract

Social tags take an important role in exploratory search. In collaborative tagging systems, users are allowed to annotate resources with tags. The significant challenges in such systems are the uncertainty of tag quality and the incomplete annotation on a large number of resources. Based on the observation that these problems can be statistically negligible after receiving sufficient tags, we propose a novel incentive mechanism to reward taggers according to the quality of their bookmarks, called the Quality-based dynamic Incentive Mechanism (QIM). To well evaluate the quality of bookmarks, we design some quantitative evaluation methods. The reward allocation function is proposed to allocate the budget to different taggers based on their bookmark quality and the tagging states of annotated resources. We perform experiments to evaluate our method on three public datasets collected from real tagging systems. Comparing with previous works, the adopted principle of “high quality deserves high price” in this paper can encourage users to annotate seriously. The experimental results show that our method gets higher tagging quality of resources under a fixed budget. Moreover, it requires less time and less money to achieve the stable tagging state of a system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://www.flickr.com

  2. http://www.bibsonomy.org

  3. http://www.goodreads.com

  4. http://ir.ii.uam.es/hetrec2011/datasets.html

References

  1. Kang, R., Fu, W.T., Kannampallil, T.G. : Exploiting knowledge-in-the-head and knowledge-in-the-social-web: effects of domain expertise on exploratory search in individual and social search environments. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 393–402 (2010)

  2. Sen, S., Harper, F.M., LaPitz, A., Riedl, J.: The quest for quality tags. In: Proceedings of the 2007 International ACM Conference on Supporting Group Work, pp. 361–370. ACM (2007)

  3. Sen, S., Vig, J., Riedl, J.: Tagommenders: Connecting users to items through tags. In: Proceedings of the 18th International Conference on World Wide Web, pp. 671–680. ACM (2009)

  4. Zubiaga, A., Fresno, V., Martinez, R., García-Plaza, A.P.: Harnessing folksonomies to produce a social classification of resources. IEEE Trans. Knowl. Data Eng. 25(8), 1801–1813 (2013)

    Article  Google Scholar 

  5. Wu, L., Yang, L., Yu, N., Hua, X.S.: Learning to tag. In: Proceedings of the 18th International Conference on World Wide Web, pp. 361–370. ACM (2009)

  6. Halpin, H., Robu, V., Shepherd, H.: The complex dynamics of collaborative tagging. In: Proceedings of the 16th International Conference on World Wide Web pp. 211–220 (2007)

  7. Marchetti, A., Tesconi, M., Ronzano, F., Rosella, M., Minutoli, S: Semkey: a semantic collaborative tagging system. In: Proceedings of the Workshop on Tagging and Metadata for Social Information Organization at WWW, vol. 7, pp. 8–12 (2007)

  8. Wetzker, R., Zimmermann, C., Bauckhage, C.: Analyzing social bookmarking systems: a del.icio.us cookbook. In: Proceedings of the ECAI 2008 Mining Social Data Workshop, pp. 26–30 (2008)

  9. Van Damme, C., Hepp, M., Coenen, T.: Quality Metrics for Tags of Broad Folksonomies. In: Proceedings of International Conference on Semantic Systems (I-SEMANTICS), pp. 118–125 (2008)

  10. Zubiaga, A., Körner, C., Strohmaier, M.: Tags vs shelves: from social tagging to social classification. In: Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia, pp. 93–102. ACM (2011)

  11. Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. J. Inform. Sci. 32(2), 198–208 (2006)

    Article  Google Scholar 

  12. Körner, C., Kern, R., Grahsl, H.-P., Strohmaier, M.: Of categorizers and describers: An evaluation of quantitative measures for tagging motivation. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, pp. 157–166. ACM (2010)

  13. Yang, X.S., Cheng, R., Mo, L., Kao, B., Cheung, D.W.: On incentive-based tagging. In: Proceedings of the 2013 IEEE International Conference on Data Engineering Data Engineering (ICDE). pp. 685–696. IEEE (2013)

  14. Kipp, M.E.I.: Convergence and divergence in tagging systems: an examination of tagging practices over a four year period. In: Proceedings of the American Society for Information Science and Technology, vol. 47(1), pp. 1–2 (2010)

  15. Hope, G., Wang, T.G., Barkataki, S.: Convergence of web 2.0 and semantic web: a semantic tagging and searching system for creating and searching blogs. In: Proceedings of the International Conference on Semantic Computing, pp. 201–208. IEEE Computer Society (2007)

  16. Lin, X., Beaudoin, J.E., Bui, Y., Desai, K.: Exploring characteristics of social classification. Adv. Classif. Res. Online 17(1), 1–19 (2006)

    Article  Google Scholar 

  17. Sood, S., Owsley, S., Hammond, K.J, Birnbaum, L.: TagAssist: automatic tag suggestion for blog posts. In: Proceedings of the International Conference on Weblogs and Social Media (2007)

  18. Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proceedings of the 17th international conference on World Wide Web, pp. 675–684. ACM (2008)

  19. Bi, B., Lee, S. D., Kao, B., Cheng, R.: CubeLSI: an effective and efficient method for searching resources in social tagging systems. In: Proceedings of the IEEE 27th International Conference on Data Engineering (ICDE), pp. 27–38. IEEE (2011)

  20. Wu, H., Zubair, M., Maly, K.: Harvesting social knowledge from folksonomies. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, pp. 111–114. ACM (2006)

  21. Lamere, P.: Social tagging and music information retrieval. J. New Music Res. 37(2), 101–114 (2008)

    Article  Google Scholar 

  22. Zeng, D., Li, H.: How useful are tags? an empirical analysis of collaborative tagging for web page recommendation. In Intelligence and Security Informatics, pp. 320–330. Springer, Berlin (2008)

  23. Heymann, P., Ramage, D., Garcia-Molina, H.: Social tag prediction. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 531–538. ACM (2008)

  24. Sigurbjörnsson, B., Van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proceedings of the 17th International Conference on World Wide Web, pp. 327–336. ACM (2008)

  25. Du, W.H., Rau, J.W., Huang, J.W., Chen, Y.S.: Improving the quality of tags using state transition on progressive image search and recommendation system. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3233–3238. IEEE (2012)

  26. Chen, H.-M., Chang, M.-H., Chang, P.-C., Tien, M.-C., Hsu, W.H., Wu, J.-Li.: SheepDog: group and tag recommendation for flickr photos by automatic search-based learning. In: Proceedings of the 16th ACM International Conference on Multimedia, pp. 737–740. ACM (2008)

  27. Krestel, R., Fankhauser, P.: Personalized topic-based tag recommendation. Neurocomputing 76(1), 61–70 (2012)

    Article  Google Scholar 

  28. Xu, Z., Fu, Y., Mao, J., Su, D.: Towards the semantic web: collaborative tag suggestions. In: Proceedings of Collaborative Web Tagging Workshop at WWW (2006)

  29. Majid, A., Khusro, S., Rauf, A.: Semantics in social tagging systems: a review. In: Proceedings of the International Conference on Computer Networks and Information Technology (ICCNIT), 191–203. IEEE (2011)

  30. Godoy, D., Rodriguez, G., Scavuzzo, F.: Leveraging semantic similarity for folksonomy-based recommendation. IEEE Internet Comput. 18(1), 1 (2013)

    Google Scholar 

  31. Mo, L., Cheng, R., Kao, B., Yang, X.S., Ren, C., Lei, S., Cheung, D.W., Lo, E.: Optimizing plurality for human intelligence tasks. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 1929–1938. ACM (2013)

  32. Lei, S., Yang, X.S., Mo, L., Maniu, S., Cheng, R.: iTag: incentive-based tagging. In: Proceedings of the IEEE 30th International Conference on Data Engineering (ICDE), pp. 1186–1189. IEEE (2014)

  33. Weng, L., Menczer, F.: GiveALink tagging game: an incentive for social annotation. In: Proceedings of the acm sigkdd Workshop on Human Computation, pp. 26–29. ACM (2010)

  34. Shokri, R., Theodorakopoulos, G., Troncoso, C., Hubaux, J.-P., Le Boudec, J.-Y.: Protecting location privacy: optimal strategy against localization attacks. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 617–627. ACM (2012)

  35. Squicciarini, A.C., Griffin, C., Sundareswaran, S.: Towards a game theoretical model for identity validation in social network sites. In: Proceedings of the International Conference on Social Computing (Socialcom), pp. 1081–1088. IEEE (2011)

  36. Görlitz, O., Sizov, S., Staab, S.: PINTS: peer-to-peer Infrastructure for Tagging Systems. In: Proceedings of the 7th International Conference on Peer-to-peer Systems, p. 19 (2008)

  37. Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in social bookmarking systems. Ai Commun. 21(4), 231–247 (2008)

    MATH  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper. We are also grateful to Dr. Reynold Cheng, from Department of Computer Science in the University of Hong Kong, for his comments and providing datasets. This work is supported by the National Natural Science Foundation of China (61173140), the National Science & Technology Pillar Program (2012BAF10B03-3), Special Program on Independent Innovation & Achievements Transformation of Shandong Province (2014ZZCX03301) and Science & Technology Development Program of Shandong Province (2014GGX101046).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, China

    Haoran Xu, Dandan Zhou, Yuqing Sun & Haiqi Sun

Authors
  1. Haoran Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dandan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuqing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haiqi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuqing Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, H., Zhou, D., Sun, Y. et al. Quality based dynamic incentive tagging. Distrib Parallel Databases 33, 69–93 (2015). https://doi.org/10.1007/s10619-014-7164-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10619-014-7164-8

Keywords

Search

Navigation