Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender

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

Abstract

We assume that recommender systems are more successful, when they are based on a thorough understanding of how people process information. In the current paper we test this assumption in the context of social tagging systems. Cognitive research on how people assign tags has shown that they draw on two interconnected levels of knowledge in their memory: on a conceptual level of semantic fields or LDA topics, and on a lexical level that turns patterns on the semantic level into words. Another strand of tagging research reveals a strong impact of time-dependent forgetting on users’ tag choices, such that recently used tags have a higher probability being reused than “older” tags. In this paper, we align both strands by implementing a computational theory of human memory that integrates the two-level conception and the process of forgetting in form of a tag recommender. Furthermore, we test the approach in three large-scale social tagging datasets that are drawn from BibSonomy, CiteULike and Flickr.

As expected, our results reveal a selective effect of time: forgetting is much more pronounced on the lexical level of tags. Second, an extensive evaluation based on this observation shows that a tag recommender interconnecting the semantic and lexical level based on a theory of human categorization and integrating time-dependent forgetting on the lexical level results in high accuracy predictions and outperforms other well-established algorithms, such as Collaborative Filtering, Pairwise Interaction Tensor Factorization, FolkRank and two alternative time-dependent approaches. We conclude that tag recommenders will benefit from going beyond the manifest level of word co-occurrences, and from including forgetting processes on the lexical level.

Keywords

Personalized tag recommendations Time-dependent recommender systems Latent Dirichlet Allocation LDA Human categorization Human memory model BibSonomy CiteULike Flickr 

References

  1. 1.
    Hintzman, D.L.: Minerva 2: a simulation model of human memory. Behav. Res. Methods Instrum. Comput. 16, 96–101 (1984)CrossRefGoogle Scholar
  2. 2.
    Kwantes, P.J.: Using context to build semantics. Psychon. Bull. Rev. 12, 703–710 (2005)CrossRefGoogle Scholar
  3. 3.
    Barsalou, L.: Situated simulation in the human conceptual system. Lang. Cogn. Process. 18, 513–562 (2003)CrossRefGoogle Scholar
  4. 4.
    Glushko, R.J., Maglio, P.P., Matlock, T., Barsalou, L.W.: Categorization in the wild. Trends Cogn. Sci. 12, 129–135 (2008)CrossRefGoogle Scholar
  5. 5.
    Seitlinger, P., Kowald, D., Trattner, C., Ley, T.: Recommending tags with a model of human categorization. In: Proceedings of CIKM ’13, pp. 2381–2386. ACM, New York (2013)Google Scholar
  6. 6.
    Polyn, S.M., Norman, K.A., Kahana, M.J.: A context maintenance and retrieval model of organizational processes in free recall. Psychol. Rev. 116, 129 (2009)CrossRefGoogle Scholar
  7. 7.
    Anderson, J.R., Schooler, L.J.: Reflections of the environment in memory. Psychol. Sci. 2, 396–408 (1991)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    Lipczak, M.: Hybrid tag recommendation in collaborative tagging systems. Ph.D. thesis, Dalhousie University (2012)Google Scholar
  15. 15.
    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
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of WSDM 2010, pp. 81–90. ACM, New York (2010)Google Scholar
  19. 19.
    Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of RecSys 2009, pp. 61–68. ACM (2009)Google Scholar
  20. 20.
    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(2), 307–325 (2012)CrossRefGoogle Scholar
  21. 21.
    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
  22. 22.
    Yin, D., Hong, L., Davison, B.D.: Exploiting session-like behaviors in tag prediction. In: Proceedings of WWW’2011, pp. 167–168. ACM (2011)Google Scholar
  23. 23.
    Brainerd, C., Reyna, V.: Recollective and nonrecollective recall. J. Mem. Lang. 63, 425–445 (2010)CrossRefGoogle Scholar
  24. 24.
    Kintsch, W., Mangalath, P.: The construction of meaning. Top. Cogn. Sci. 3, 346–370 (2011)CrossRefGoogle Scholar
  25. 25.
    Krestel, R., Fankhauser, P.: Tag recommendation using probabilistic topic models. In: ECML PKDD Discovery Challenge 2009 (DC09), p. 131 (2009)Google Scholar
  26. 26.
    Lorince, J., Todd, P.M.: Can simple social copying heuristics explain tag popularity in a collaborative tagging system? In: Proceedings of WebSci ’13, pp. 215–224. ACM, New York (2013)Google Scholar
  27. 27.
    Floeck, F., Putzke, J., Steinfels, S., Fischbach, K., Schoder, D.: Imitation and quality of tags in social bookmarking systems-collective intelligence leading to folksonomies. In: Bastiaens, T.J., Baumöl, U., Krämer, B.J. (eds.) On Collective Intelligence. AISC, vol. 76, pp. 75–91. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Seitlinger, P., Ley, T.: Implicit imitation in social tagging: familiarity and semantic reconstruction. In: Proceedings of CHI ’12, pp. 1631–1640. ACM, New York (2012)Google Scholar
  29. 29.
    Helic, D., Körner, C., Granitzer, M., Strohmaier, M., Trattner, C.: Navigational efficiency of broad vs. narrow folksonomies. In: Proceedings of HT ’12, pp. 63–72. ACM, New York (2012)Google Scholar
  30. 30.
    Gemmell, J., Schimoler, T., Ramezani, M., Christiansen, L., Mobasher, B.: Improving folkrank with item-based collaborative filtering. In: Recommender Systems & the Social Web (2009)Google Scholar
  31. 31.
    Doerfel, S., Jäschke, R.: An analysis of tag-recommender evaluation procedures. In: Proceedings of RecSys ’13, pp. 343–346. ACM, New York (2013)Google Scholar
  32. 32.
    Campos, P.G., Díez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model. User-Adap. Inter. 24(1–2), 67–119 (2013)Google Scholar
  33. 33.
    Van Rijsbergen, C.J.: Foundation of evaluation. J. Doc. 30, 365–373 (1974)CrossRefGoogle Scholar
  34. 34.
    Balby Marinho, L., Hotho, A., Jschke, R., Nanopoulos, A., Rendle, S., Schmidt-Thieme, L., Stumme, G., Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer Briefs in Electrical and Computer Engineering. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  35. 35.
    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
  36. 36.
    Parra-Santander, D., Brusilovsky, P.: Improving collaborative filtering in social tagging systems for the recommendation of scientific articles. In: Proceedings of WI-IAT 2010, vol. 1, pp. 136–142. IEEE (2010)Google Scholar
  37. 37.
    Fu, W.T., Dong, W.: Collaborative indexing and knowledge exploration: a social learning model. IEEE Intell. Syst. 27, 39–46 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dominik Kowald
    • 1
    • 2
  • Paul Seitlinger
    • 2
  • Simone Kopeinik
    • 2
  • Tobias Ley
    • 3
  • 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