A Timeline-Based Algorithm for Personalized Tag Recommendation

  • Zhaohui Yu
  • Puwei Wang
  • Xiaoyong Du
  • Jianwei Cui
  • Tianren Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6724)


Recently, tagging has been a flexible and important way to share and categorize web resources, these user-generated tags are effective to represent user interests because these tags reflect human being’s judgments while more concise and closer to human understanding, and the user interests are changing over time. Thus, modeling user interests to meet individual user needs is an important challenge for personalization and information filtering applications, such as recommender systems. In this paper, we apply a distance decay model for modeling user interests in terms of tags based on timeline. We then propose a novel algorithm to measure users’ similarities in terms of their tagging activity over a specific time period and provide personalized tag recommendation according to similar users’ interests in their next time intervals. Experimental results demonstrate the higher precision and recall with our personalized tag recommendation algorithm than other existing methods.


timeline user interests distance decay model personalized tag recommendation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Koutsonikola, V., Vakali, A., Giannakidou, E., Kompatsiaris, I.: Clustering of Social Tagging System Users: A Topic and Time Based Approach. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 75–86. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Dubinko, M., Kumar, R., Magnani, J., Novak, J., Raghavan, P., Tomkins, A.: Visualizing tags over time. In: WWW 2006, pp. 193–202 (2006)Google Scholar
  3. 3.
    Bilenko, M., et al.: Talking the talk vs. walking the walk: salience of information needs in querying vs.browsing. In: Proc. ACM SIGIR, pp. 705–706 (2008)Google Scholar
  4. 4.
    Xing, C., Gao, F., Zhan, S., Zhou, l.: A collaborative Filtering Recommendation Algorithm Incorporated with User Interest Change. Journal of Computer Research and Development 44(2), 296–301 (2007)CrossRefGoogle Scholar
  5. 5.
  6. 6.,
  7. 7.
  8. 8.
    Zeng, C., Xing, C.-X., Zhou, L.-Z., et al.: Similarity measure and instance selection for collaborative filtering international. Journal of Electronic Commerce 4(8), 115–129 (2004)Google Scholar
  9. 9.
    Kits, B., Freed, D., Vrieze, M.: Cross-sell: A fast promotion-tunable customer-item recommendation method based on conditional independent probabilities. In: Proc of ACM SIGKDD Int’l Conf., pp. 437–446. ACM Press, New York (2000)Google Scholar
  10. 10.
    Taylor, P.J.: Distance transformation and distance decay functions: Geographical Analysis, 221-238 (1971)Google Scholar
  11. 11.
    Balabanovic, M., Shoham, Y.: Learning information retrieval agents: Experiments with automated web browsing. In: Proceedings of the AAAI Spring Symposium on Information Gathering from Heteroge- nous, Distributed Resources, Stanford,CA, USA, pp. 13–18 (1995)Google Scholar
  12. 12.
    Sorensen, H., Mcelligot, M.: Psun: A profiling system for usenet news. In: CKIM 1995 Workshop on Intelligent Information Agents (1995)Google Scholar
  13. 13.
    Godoy, D., Amandi, A.: User profiling in personal information agents: a survey. Knowl. Eng. Rev. 20(4), 329–361 (2005)CrossRefzbMATHGoogle Scholar
  14. 14.
    Kook, H.J.: Profiling multiple domains of user interests and using them for personalized web support. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005, Part II. LNCS, vol. 3645, pp. 512–520. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Diederich, J., Iofciu, T.: Finding communities of practice from user profiles based on folksonomies. In: Proceedings of the 1st International Workshop on Building Technology Enhanced Learning solutions for Communities of Practice (2006)Google Scholar
  16. 16.
    Michlmayr, E., Cayzer, S.: Learning user profiles from tagging data and leveraging them for personalized information access. In: Proceedings of the Workshop on Tagging and Metadata for Social Information Organization, co-located with the 16th International World Wide Web Conference, Banff, Alberta, Canada, May 8-12 (May 2, 2007)Google Scholar
  17. 17.
    Dupret, G., Murdock, V., Piwowarski: Web search evaluation using clickthrough data and a user model. In: Proc. WWW Workshop on Query Log Analysis (2007)Google Scholar
  18. 18.
    Ingwersen, P., Järvelin, K.: The Turn: Integration of Information Seeking and Retrieval in Context. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  19. 19.
    Schafer, J., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proc of ACM E-Commerce, pp. 158–166. ACM Press, New York (1999)Google Scholar
  20. 20.
    Jayawardana, C., Priyantha Hewagamage, K., HIrakawa, M.: A personalized information environment for digital libraries. Infromation Technology and Libraries 20(4), 185–196 (2001)Google Scholar
  21. 21.
    Konstan, J., Miller, B., Maltz, D., et al.: GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  22. 22.
    Fengrong, G.: Research on the key techniques of personalized recommender systems: [Ph D dissertation]. Renmin University of China, Beijing (2003)Google Scholar
  23. 23.
    Linden, G., Smith, B., York, J.: recommendations: Item-to-Item collaborative filtering. IEEE Internet Computering 7(1), 76–80 (2003)CrossRefGoogle Scholar
  24. 24.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Zhaohui Yu
    • 1
    • 2
  • Puwei Wang
    • 1
    • 2
  • Xiaoyong Du
    • 1
    • 2
  • Jianwei Cui
    • 1
    • 2
  • Tianren Xu
    • 1
    • 2
  1. 1.Key Labs of Data Engineering and Knowledge Engineering Ministry of EducationChina
  2. 2.School of InformationRenmin University of ChinaBeijingChina

Personalised recommendations