Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context

  • Dominik Kowald
  • Simone Kopeinik
  • Paul Seitlinger
  • Tobias Ley
  • Dietrich Albert
  • Christoph Trattner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8940)


In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. Based on a theory of human memory, the approach estimates a tag’s probability being applied by a particular user as a function of usage frequency and recency of the tag in the user’s past. This probability is further refined by considering the influence of the current semantic context of the user’s tagging situation. Using three real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike and Flickr, we show how refining frequency-based estimates by considering usage recency and contextual influence outperforms conventional “most popular tags” approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism.

By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as FolkRank, Pairwise Interaction Tensor Factorization and Collaborative Filtering. We conclude that our approach provides an accurate and computationally efficient model of a user’s temporal tagging behavior. We demonstrate how effective principles of recommender systems can be designed and implemented if human memory processes are taken into account.


Personalized tag recommendations Time-dependent recommender systems Base-level learning equation ACT-R Human memory model BibSonomy CiteULike Flickr 



This work is supported by the Know-Center, the EU funded projects Learning Layers (Grant Agreement 318209) and weSPOT (Grant Agreement 318499) and the Austrian Science Fund (FWF): P 25593-G22. Moreover, parts of this work were carried out during the tenure of an ERCIM “Alain Bensoussan” fellowship programme. The Know-Center is funded within the Austrian COMET Program - Competence Centers for Excellent Technologies - under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency (FFG).


  1. 1.
    Halpin, H., Robu, V., Shepherd, H.: The complex dynamics of collaborative tagging. In: Proceedings of the 16th International Conference on World Wide Web. WWW ’07, pp. 211–220. ACM, New York (2007)Google Scholar
  2. 2.
    Steels, L.: Semiotic dynamics for embodied agents. IEEE Intell. Syst. 21, 32–38 (2006)CrossRefGoogle Scholar
  3. 3.
    Marlow, C., Naaman, M., Boyd, D., Davis, M.: Ht06, tagging paper, taxonomy, flickr, academic article, to read. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia. HYPERTEXT ’06, pp. 31–40. ACM, New York (2006)Google Scholar
  4. 4.
    Anderson, J.R., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychol. Rev. 111, 1036–1050 (2004)CrossRefGoogle Scholar
  5. 5.
    Anderson, J.R., Schooler, L.J.: Reflections of the environment in memory. Psychol. Sci. 2, 396–408 (1991)CrossRefGoogle Scholar
  6. 6.
    Held, C., Kimmerle, J., Cress, U.: Learning by foraging: the impact of individual knowledge and social tags on web navigation processes. Comput. Hum. Behav. 28, 34–40 (2012)CrossRefGoogle Scholar
  7. 7.
    Dellschaft, K., Staab, S.: An epistemic dynamic model for tagging systems. In: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia. HT ’08, pp. 71–80. ACM, New York (2008)Google Scholar
  8. 8.
    Cattuto, C., Loreto, V., Pietronero, L.: Semiotic dynamics and collaborative tagging. Proc. Natl. Acad. Sci. 104, 1461–1464 (2007)CrossRefGoogle Scholar
  9. 9.
    Pirolli, P.L., Anderson, J.R.: The role of practice in fact retrieval. J. Exp. Psychol. Learn. Mem. Cogn. 11, 136 (1985)CrossRefGoogle Scholar
  10. 10.
    Zhang, L., Tang, J., Zhang, M.: Integrating temporal usage pattern into personalized tag prediction. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds.) APWeb 2012. LNCS, vol. 7235, pp. 354–365. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Lorince, J., Todd, P.M.: Can simple social copying heuristics explain tag popularity in a collaborative tagging system? In: Proceedings of the 5th Annual ACM Web Science Conference. WebSci ’13, pp. 215–224. ACM, New York (2013)Google Scholar
  12. 12.
    Lipczak, M.: Hybrid tag recommendation in collaborative tagging systems. Ph.D. thesis, Dalhousie University (2012)Google Scholar
  13. 13.
    Kowald, D., Seitlinger, P., Trattner, C., Ley, T.: Long time no see: The probability of reusing tags as a function of frequency and recency. In: Proceedings of WWW ’14, ACM, New York, (2014)Google Scholar
  14. 14.
    Körner, C., Benz, D., Hotho, A., Strohmaier, M., Stumme, G.: Stop thinking, start tagging: tag semantics emerge from collaborative verbosity. In: Proceedings of the 19th International Conference on World Wide Web. WWW ’10, pp. 521–530. ACM, New York (2010)Google Scholar
  15. 15.
    Helic, D., Trattner, C., Strohmaier, M., Andrews, K.: Are tag clouds useful for navigation? a network-theoretic analysis. Int. J. Soc. Comput. Cyber-Phys. Syst. 1, 33–55 (2011)CrossRefGoogle Scholar
  16. 16.
    Trattner, C., Lin, Y.l., Parra, D., Yue, Z., Real, W., Brusilovsky, P.: Evaluating tag-based information access in image collections. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media, pp. 113–122. ACM (2012)Google Scholar
  17. 17.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)Google Scholar
  19. 19.
    Hamouda, S., Wanas, N.: Put-tag: personalized user-centric tag recommendation for social bookmarking systems. Soc. Netw. Anal. Min. 1, 377–385 (2011)CrossRefGoogle Scholar
  20. 20.
    Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining. WSDM ’10, pp. 81–90. ACM, New York (2010)Google Scholar
  21. 21.
    Wetzker, R., Zimmermann, C., Bauckhage, C., Albayrak, S.: I tag, you tag: translating tags for advanced user models. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 71–80. ACM (2010)Google Scholar
  22. 22.
    Krestel, R., Fankhauser, P.: Language models and topic models for personalizing tag recommendation. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 82–89. IEEE (2010)Google Scholar
  23. 23.
    Rawashdeh, M., Kim, H.N., Alja’am, J.M., El Saddik, A.: Folksonomy link prediction based on a tripartite graph for tag recommendation. J. Intell. Inf. Syst. 40, 1–19 (2012)Google Scholar
  24. 24.
    Troussov, A., Parra, D., Brusilovsky, P.: Spreading activation approach to tag-aware recommenders: modeling similarity on multidimensional networks. In: Proceedings of Workshop on Recommender Systems and the Social Web at the 2009 ACM Conference on Recommender Systems, RecSys, vol. 9 (2009)Google Scholar
  25. 25.
    Stanley, C., Byrne, M.D.: Predicting tags for stackoverflow posts. In: Proceedings of ICCM (2013)Google Scholar
  26. 26.
    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)Google Scholar
  27. 27.
    Yin, D., Hong, L., Xue, Z., Davison, B.D.: Temporal dynamics of user interests in tagging systems. In: Twenty-Fifth AAAI Conference on Artificial Intelligence (2011)Google Scholar
  28. 28.
    Yin, D., Hong, L., Davison, B.D.: Exploiting session-like behaviors in tag prediction. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 167–168. ACM (2011)Google Scholar
  29. 29.
    McAuley, J., Leskovec, J.: Hidden factors and hidden topics: Understanding rating dimensions with review text. In: Proceedings of the ACM Conference Series on Recommender Systems, ACM, New York (2013)Google Scholar
  30. 30.
    Van Maanen, L., Marewski, J.N.: Recommender systems for literature selection: A competition between decision making and memory models. In: Proceedings of the 31st Annual Conference of the Cognitive Science Society, pp. 2914–2919 (2009)Google Scholar
  31. 31.
    Kowald, D., Lacic, E., Trattner, C.: Tagrec: Towards a standardized tag recommender benchmarking framework. In: Proceedings of HT’14, ACM, New York (2014)Google Scholar
  32. 32.
    Helic, D., Körner, C., Granitzer, M., Strohmaier, M., Trattner, C.: Navigational efficiency of broad vs. narrow folksonomies. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media. HT ’12, pp. 63–72. ACM, New York (2012)Google Scholar
  33. 33.
    Gemmell, J., Schimoler, T., Ramezani, M., Christiansen, L., Mobasher, B.: Improving folkrank with item-based collaborative filtering. In: Proceedings of theWorkshop on Recommender Systems and the Social Web (RSWEB ’09), pp. 17–24. New York, NY, USA (2009)Google Scholar
  34. 34.
    Batagelj, V., Zaveršnik, M.: Generalized cores. arXiv preprint cs/0202039 (2002)
  35. 35.
    Doerfel, S., Jäschke, R.: An analysis of tag-recommender evaluation procedures. In: Proceedings of the 7th ACM Conference on Recommender Systems. RecSys ’13, pp. 343–346. ACM, New York (2013)Google Scholar
  36. 36.
    Campos, P.G., Díez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model. User-Adapt. Interact. 24, 1–53 (2013)Google Scholar
  37. 37.
    Marinho, L., Nanopoulos, A., Schmidt-Thieme, L., Jäschke, R., Hotho, A., Stumme, G., Symeonidis, P.: Social tagging recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 615–644. Springer, New York (2011)Google Scholar
  38. 38.
    Marinho, L.B., Hotho, A., Jäschke, R., Nanopoulos, A., Rendle, S., Schmidt-Thieme, L., Stumme, G., Symeonidis, P.: Recommender Systems for Social Tagging Systems. SpringerBriefs in Electrical and Computer Engineering. Springer, New York (2012)CrossRefGoogle Scholar
  39. 39.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in social bookmarking systems. AI Commun. 21, 231–247 (2008)zbMATHMathSciNetGoogle Scholar
  40. 40.
    Marinho, L.B., Schmidt-Thieme, L.: Collaborative tag recommendations. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds.) Data Analysis, Machine Learning and Applications, pp. 533–540. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  41. 41.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  42. 42.
    Rendle, S.: Factorization machines. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 995–1000. IEEE (2010)Google Scholar
  43. 43.
    Lipczak, M., Milios, E.: The impact of resource title on tags in collaborative tagging systems. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia. HT ’10, pp. 179–188. ACM, New York (2010)Google Scholar
  44. 44.
    Seitlinger, P., Kowald, D., Trattner, C., Ley, T.: Recommending tags with a model of human categorization. In: The ACM International Conference on Information and Knowledge Managament, ACM, New York (2013)Google Scholar
  45. 45.
    Gemmell, J., Schimoler, T., Mobasher, B., Burke, R.: Hybrid tag recommendation for social annotation systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 829–838. ACM (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dominik Kowald
    • 1
    • 2
  • Simone Kopeinik
    • 2
  • Paul Seitlinger
    • 2
  • Tobias Ley
    • 3
  • Dietrich Albert
    • 2
  • Christoph Trattner
    • 4
  1. 1.Know-CenterGraz University of TechnologyGrazAustria
  2. 2.Knowledge Technologies InstituteGraz University of TechnologyGrazAustria
  3. 3.Institute of InformaticsTallin UniversityTallinnEstonia
  4. 4.Norwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations