The Journal of Web Science > Vol 2 > Issue 1

Modeling Activation Processes in Human Memory to Predict the Use of Tags in Social Bookmarking Systems

Christoph Trattner, NTNU and Know-Center, Norway and Austria, ctrattner@know-center.at Dominik Kowald, Know-Center, Graz University of Technology, Austria, dkowald@know-center.at Paul Seitlinger, Graz University of Technology, Austria, paul.seitlinger@tugraz.at Tobias Ley, Tallinn University, Estonia, tley@tlu.ee Simone Kopeinik, Graz University of Technology, Austria, simone.kopeinik@tugraz.at
 
Suggested Citation
Christoph Trattner, Dominik Kowald, Paul Seitlinger, Tobias Ley and Simone Kopeinik (2016), "Modeling Activation Processes in Human Memory to Predict the Use of Tags in Social Bookmarking Systems", The Journal of Web Science: Vol. 2: No. 1, pp 1-16. http://dx.doi.org/10.1561/106.00000004

Published: 25 Mar 2016
© 2016 C. Trattner, D. Kowald, P. Seitlinger, S. Kopeinik, and T. Ley
 
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This is published under the terms of CC BY-NC-ND 2.0.

In this article:
1. Introduction
2. Related Work
3. Modeling Recency Effects in Social Tagging Systems
4. A Tag Recommender Based on Activation in Memory
5. Experimental Setup
6. Results and Discussion
7. Conclusion and Future Work
8. Reproducibility
A. Appendix: Validation of Data Sampling
Acknowledgments
References

Abstract

In recent years, several successful tag recommendation mechanisms have been developed that, among others, built upon Collaborative Filtering, Tensor Factorization, graph-based algorithms and simple “most popular tags” approaches. From an economic perspective, the latter approach has been convincing as calculating frequencies is computationally efficient and has shown to be effective with respect to different recommender evaluation metrics. In order to extend these conventional “most popular tags” approaches 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 reuse probability as a function of usage frequency and recency in the user’s past (base-level activation) as well as of the current semantic context (associative component). Using four real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike, Delicious and Flickr, we show how refining frequency-based estimates, by considering recency and semantic context, 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 resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as Collaborative Filtering, FolkRank and Pairwise Interaction Tensor Factorization with respect to recommender accuracy and runtime. We conclude that our approach provides an accurate and computationally efficient model of a user’s temporal tagging behavior. Moreover, we demonstrate how effective principles of recommender systems can be designed and implemented if human memory processes are taken into account.

DOI:10.1561/106.00000004