Recommending Tags with a Model of Human Categorization


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Social tagging involves complex processes of human categorization that have been the topic of much research in the cognitive sciences. In this paper we present a recommender approach for social tags whose principles are derived from some of the more prominent and empirically well-founded models from this research tradition. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific re- source, which are either 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 at- tribute this to the fact that our approach processes seman- tic information (either latent topics or external categories) across the three different layers, and this substantially enhances the recommendation performance. 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.

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Recommending Tags with a Model of Human Categorization

  1. 1. http://Learning-Layers-eu 1
  2. 2. Thanks to Paul Seitlinger Graz University of Technology Austria Dominik Kowald Graz University of Technology Austria Tobias Ley Tallinn University Estonia
  3. 3. What will this talk all about? It will be about tags… social tags! human cognition …and how to predict tags 
  4. 4. Motivation I assume all of you agree that social tags are cool • They help you to classify Web content better [Zubiaga 2012] • They help you to navigate large knowledge repositories better [Helic et al. 2012] • They help you to search for information faster [Trattner et al. 2012] However, there is an issue with social tags… People are typically lazy to apply social tags(!!) Zubiaga, A. (2012). Harnessing Folksonomies for Resource Classification. arXiv preprint arXiv:1204.6521. Helic, D., Körner, C., Granitzer, M., Strohmaier, M., & Trattner, C. (2012, June). Navigational efficiency of broad vs. narrow folksonomies. In Proceedings of the 23rd ACM conference on Hypertext and social media (pp. 63-72). ACM. Trattner, C., Lin, Y. L., Parra, D., Yue, Z., Real, W., & Brusilovsky, P. (2012, June). Evaluating tag-based information access in image collections. In Proceedings of the 23rd ACM conference on Hypertext and social media (pp. 113122). ACM.
  5. 5. Motivation To overcome that issue some smart people started to invent mechanisms that should help the user in applying tags, known as social tag recommender… system based on: • Tag Frequencies • MostPopular approaches [Hotho et al. 2006] • Collaborative Filtering • User based and resource based CF [Marinho et al. 2008] • Graph Structures • Adapted PageRank and FolkRank [Hotho et al. 2006] • Topic Models • Latent Dirichlet Allocation (LDA) [Krestel et al. 2009,2010]  These approaches lack of theoretical grounding
  6. 6. Why do we need a cognitive model? Well the first answer I always get from my psychological friends is that we do not like data pure data driven approaches… Me: OK The second answer I get is that with cognitive we can understand things better…why is something happening and how.
  7. 7. Approach • Based on a Human cognition (derived from ALCOVE [Kruschke et al., 1992]) • Three Layers model • Layer 1 (Input layer) • Encodes semantic features (external categories or LDA topics) • Layer 2 (Hidden layer) • Categorizes user-specific resources by the encoded semantic features • Layer 3 (Output layer) • Samples tags based on the proceeding categorization processes
  8. 8. Evaluation In order to evaluate our approach we used a Dataset of delicious available in • Wikipedia • p-core pruning (p = 14) • To finally measure to performance of our approach we split up our dataset in two sub-sets 80% for training and 20% for testing Training • Precision, Recall, F1-score, MRR, MAP • As Baseline algorithm we have chosen Latent Dirichlet Allocation (LDA) [Krestel et al. 2009]
  9. 9. Results (1)
  10. 10. Results (2)
  11. 11. Conclusions • We introduced a new approach called 3 Layers which is based on a model of human cognition • To evaluate our approach, we used a dataset of delicious tags present in the English Wikipedia • To test the performance of our approach we compared it to a LDA-based recommender •Based on a 80/20 fold cross validation approach we could show that our approach outperforms the LDA-based recommender significantly
  12. 12. What are we currently working on? •In particular, we are testing potentials of ACT-R model from [Anderson et al. 2004] to predict tags. •Basically, the ACT-R model is a model about how the human memory works…and can be simulated J. R. Anderson, M. D. Byrne, S. Douglass, C. Lebiere, and Y. Qin. An integrated theory of the mind. Psychological Review, 111(4):1036–1050, 2004.
  13. 13. What are we currently working on?
  14. 14. What are we currently working on? CiteULike BibSonomy
  15. 15. What are we currently working on? LastFM Flickr
  16. 16. Thanks for your attention! Questions? Christoph Trattner Email: Web: Twitter: @ctrattner