Recommending tags with a model of human categorization

ABSTRACT

When interacting with social tagging systems, humans exercise complex processes of categorization that have been the topic of much research in cognitive science. In this paper we present a recommender approach for social tags derived from ALCOVE, a model of human category learning. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific resource, such as latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We attribute this to the fact that our approach processes semantic information (either latent topics or external categories) across the three different layers. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.

References

1. V. Batagelj and M. Zaversnik. Generalized cores. arXiv preprint cs/0202039, 2002.
2. C. Brainerd and V. Reyna. Recollective and nonrecollective recall. Journal of memory and language, 63(3):425--445, 2010.
3. U. Cress, C. Held, and J. Kimmerle. The collective knowledge of social tags: Direct and indirect influences on navigation, learning, and information processing. Comput. Educ., 60(1):59--73, Jan. 2013.
4. W.-T. Fu, T. Kannampallil, R. Kang, and J. He. Semantic imitation in social tagging. ACM Transactions on Computer-Human Interaction (TOCHI), 17(3):12, 2010.
5. T. L. Griffiths, M. Steyvers, J. B. Tenenbaum, et al. Topics in semantic representation. Psychological review, 114(2):211, 2007.
6. H. Halpin, V. Robu, and H. Shepherd. The complex dynamics of collaborative tagging. In Proceedings of the 16th international conference on World Wide Web, WWW '07, pages 211--220, New York, NY, USA, 2007. ACM.
7. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5--53, Jan. 2004.
8. P. Heymann, D. Ramage, and H. Garcia-Molina. Social tag prediction. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 531--538. ACM, 2008.
9. A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Information retrieval in folksonomies: Search and ranking. In The semantic web: research and applications, pages 411--426. Springer, 2006.
10. R. Jäschke, L. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in folksonomies. In Knowledge Discovery in Databases: PKDD 2007, pages 506--514. Springer, 2007.
11. C. Körner, R. Kern, H.-P. Grahsl, and M. Strohmaier. Of categorizers and describers: an evaluation of quantitative measures for tagging motivation. In Proceedings of the 21st ACM conference on Hypertext and hypermedia, HT '10, pages 157--166, New York, NY, USA, 2010. ACM.
12. R. Krestel and P. Fankhauser. Language models and topic models for personalizing tag recommendation. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on, volume 1, pages 82--89. IEEE, 2010.
13. R. Krestel, P. Fankhauser, and W. Nejdl. Latent dirichlet allocation for tag recommendation. In Proceedings of the third ACM conference on Recommender systems, pages 61--68. ACM, 2009.
14. J. K. Kruschke et al. Alcove: An exemplar-based connectionist model of category learning. Psychological review, 99(1):22--44, 1992.
15. M. D. Lee and D. J. Navarro. Extending the alcove model of category learning to featural stimulus domains. Psychonomic Bulletin & Review, 9(1):43--58, 2002.
16. W. J. Levelt. A theory of lexical access in speech production. In Proceedings of the 16th conference on Computational linguistics-Volume 1, pages 3--3. Association for Computational Linguistics, 1996.
17. M. Lipczak. Hybrid Tag Recommendation in Collaborative Tagging Systems. PhD thesis, Dalhousie University, 2012.
18. M. Lipczak and E. Milios. Learning in efficient tag recommendation. In Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pages 167--174, New York, NY, USA, 2010. ACM.
19. D. Milne and I. H. Witten. Learning to link with wikipedia. In Proceedings of the 17th ACM conference on Information and knowledge management, pages 509--518. ACM, 2008.
20. M. Rawashdeh, H.-N. Kim, J. M. Alja'am, and A. El Saddik. Folksonomy link prediction based on a tripartite graph for tag recommendation. Journal of Intelligent Information Systems, pages 1--19, 2012.
21. S. Rendle and L. Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pages 81--90, New York, NY, USA, 2010. ACM.
22. P. Seitlinger and T. Ley. Implicit imitation in social tagging: familiarity and semantic reconstruction. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, pages 1631--1640. ACM, 2012.
23. R. N. Shepard et al. Toward a universal law of generalization for psychological science. Science, 237(4820):1317--1323, 1987.
24. B. Sigurbjörnsson and R. van Zwol. Flickr tag recommendation based on collective knowledge. In Proceedings of the 17th international conference on World Wide Web, WWW '08, pages 327--336, New York, NY, USA, 2008. ACM.
25. Z. Xu, Y. Fu, J. Mao, and D. Su. Towards the semantic web: Collaborative tag suggestions. In Proceedings of Collaborative Web Tagging Workshop at 15th International World Wide Web Conference, 2006.
26. A. Zubiaga, V. Fresno, R. Martínez, and A. P. García-Plaza. Harnessing folksonomies to produce a social classification of resources. IEEE Transactions on Knowledge and Data Engineering, 99(PrePrints), 2012.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction

  • Information systems applications

    • Data mining