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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We define a bookmark (also known as “post”) as the set of tags a target user has assigned to a target resource at a specific time, and the personomy as a collection of all bookmarks of a user.
- 2.
- 3.
- 4.
- 5.
- 6.
Note: We used the same dataset samples as in our previous work [9], except for CiteULike, where we used a smaller sample for reasons of computational effort in respect to the calculation of the LDA topics.
- 7.
- 8.
- 9.
- 10.
References
Hintzman, D.L.: Minerva 2: a simulation model of human memory. Behav. Res. Methods Instrum. Comput. 16, 96–101 (1984)
Kwantes, P.J.: Using context to build semantics. Psychon. Bull. Rev. 12, 703–710 (2005)
Barsalou, L.: Situated simulation in the human conceptual system. Lang. Cogn. Process. 18, 513–562 (2003)
Glushko, R.J., Maglio, P.P., Matlock, T., Barsalou, L.W.: Categorization in the wild. Trends Cogn. Sci. 12, 129–135 (2008)
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)
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)
Anderson, J.R., Schooler, L.J.: Reflections of the environment in memory. Psychol. Sci. 2, 396–408 (1991)
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)
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)
Anderson, J.R., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychol. Rev. 111, 1036–1050 (2004)
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)
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)
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)
Lipczak, M.: Hybrid tag recommendation in collaborative tagging systems. Ph.D. thesis, Dalhousie University (2012)
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)
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)
Hamouda, S., Wanas, N.: Put-tag: personalized user-centric tag recommendation for social bookmarking systems. Soc. Netw. Anal. Min. 1, 377–385 (2011)
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)
Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of RecSys 2009, pp. 61–68. ACM (2009)
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)
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)
Yin, D., Hong, L., Davison, B.D.: Exploiting session-like behaviors in tag prediction. In: Proceedings of WWW’2011, pp. 167–168. ACM (2011)
Brainerd, C., Reyna, V.: Recollective and nonrecollective recall. J. Mem. Lang. 63, 425–445 (2010)
Kintsch, W., Mangalath, P.: The construction of meaning. Top. Cogn. Sci. 3, 346–370 (2011)
Krestel, R., Fankhauser, P.: Tag recommendation using probabilistic topic models. In: ECML PKDD Discovery Challenge 2009 (DC09), p. 131 (2009)
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)
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)
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)
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)
Gemmell, J., Schimoler, T., Ramezani, M., Christiansen, L., Mobasher, B.: Improving folkrank with item-based collaborative filtering. In: Recommender Systems & the Social Web (2009)
Doerfel, S., Jäschke, R.: An analysis of tag-recommender evaluation procedures. In: Proceedings of RecSys ’13, pp. 343–346. ACM, New York (2013)
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)
Van Rijsbergen, C.J.: Foundation of evaluation. J. Doc. 30, 365–373 (1974)
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)
Kowald, D., Lacic, E., Trattner, C.: Tagrec: towards a standardized tag recommender benchmarking framework. In: Proceedings of HT’14. ACM, New York (2014)
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)
Fu, W.T., Dong, W.: Collaborative indexing and knowledge exploration: a social learning model. IEEE Intell. Syst. 27, 39–46 (2012)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Kowald, D., Seitlinger, P., Kopeinik, S., Ley, T., Trattner, C. (2015). Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender. 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_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-14723-9_5
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)