Skip to main content
Book cover

International Workshop on Mining Ubiquitous and Social Environments

International Workshop on Modeling Social Media

MUSE 2013, MSM 2013: Mining, Modeling, and Recommending 'Things' in Social Media pp 55–74Cite as

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

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8940)

Abstract

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.

Keywords

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

Parts of this work have been included as an extended version in the article “Modeling Activation Processes in Human Memory to Predict the Reuse of Tags” submitted to the The Journal of Web Science.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-14723-9_4
  • Chapter length: 20 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   39.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-14723-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   49.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.

Notes

  1. 1.

    https://github.com/learning-layers/TagRec/.

  2. 2.

    http://www.kde.cs.uni-kassel.de/bibsonomy/dumps.

  3. 3.

    http://www.citeulike.org/faq/data.adp.

  4. 4.

    http://www.tagora-project.eu/.

  5. 5.

    \(F_1\)@\(5\) was also used as the main performance metric in the ECML PKDD Discovery Challenge 2009: http://www.kde.cs.uni-kassel.de/ws/dc09/.

  6. 6.

    http://www.kde.cs.uni-kassel.de/code.

  7. 7.

    http://www.informatik.uni-konstanz.de/rendle/software/tag-recommender/.

References

  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. Steels, L.: Semiotic dynamics for embodied agents. IEEE Intell. Syst. 21, 32–38 (2006)

    CrossRef  Google Scholar 

  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. Anderson, J.R., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychol. Rev. 111, 1036–1050 (2004)

    CrossRef  Google Scholar 

  5. Anderson, J.R., Schooler, L.J.: Reflections of the environment in memory. Psychol. Sci. 2, 396–408 (1991)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. Cattuto, C., Loreto, V., Pietronero, L.: Semiotic dynamics and collaborative tagging. Proc. Natl. Acad. Sci. 104, 1461–1464 (2007)

    CrossRef  Google Scholar 

  9. Pirolli, P.L., Anderson, J.R.: The role of practice in fact retrieval. J. Exp. Psychol. Learn. Mem. Cogn. 11, 136 (1985)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. Lipczak, M.: Hybrid tag recommendation in collaborative tagging systems. Ph.D. thesis, Dalhousie University (2012)

    Google Scholar 

  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. 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. 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)

    CrossRef  Google Scholar 

  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. 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)

    CrossRef  Google Scholar 

  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. Hamouda, S., Wanas, N.: Put-tag: personalized user-centric tag recommendation for social bookmarking systems. Soc. Netw. Anal. Min. 1, 377–385 (2011)

    CrossRef  Google Scholar 

  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. 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. 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. 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. 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. Stanley, C., Byrne, M.D.: Predicting tags for stackoverflow posts. In: Proceedings of ICCM (2013)

    Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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. Batagelj, V., Zaveršnik, M.: Generalized cores. arXiv preprint cs/0202039 (2002)

  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. 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. 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. 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)

    CrossRef  Google Scholar 

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

    MATH  MathSciNet  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  42. Rendle, S.: Factorization machines. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 995–1000. IEEE (2010)

    Google Scholar 

  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. 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. 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 

Download references

Acknowledgments

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).

Author information

Authors and Affiliations

  1. Know-Center, Graz University of Technology, Graz, Austria

    Dominik Kowald

  2. Knowledge Technologies Institute, Graz University of Technology, Graz, Austria

    Dominik Kowald, Simone Kopeinik, Paul Seitlinger & Dietrich Albert

  3. Institute of Informatics, Tallin University, Tallinn, Estonia

    Tobias Ley

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Christoph Trattner

Authors
  1. Dominik Kowald
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simone Kopeinik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul Seitlinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tobias Ley
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dietrich Albert
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christoph Trattner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dominik Kowald .

Editor information

Editors and Affiliations

  1. University of Kassel, Kassel, Germany

    Martin Atzmueller

  2. Microsoft, Beijing, China

    Alvin Chin

  3. University of Kassel, Kassel, Germany

    Christoph Scholz

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Christoph Trattner

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Kowald, D., Kopeinik, S., Seitlinger, P., Ley, T., Albert, D., Trattner, C. (2015). Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context. In: Atzmueller, M., Chin, A., Scholz, C., Trattner, C. (eds) Mining, Modeling, and Recommending 'Things' in Social Media. MUSE MSM 2013 2013. Lecture Notes in Computer Science(), vol 8940. Springer, Cham. https://doi.org/10.1007/978-3-319-14723-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14723-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14722-2

  • Online ISBN: 978-3-319-14723-9

  • eBook Packages: Computer ScienceComputer Science (R0)