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Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context

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Mining, Modeling, and Recommending 'Things' in Social Media (MUSE 2013, MSM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8940))

Abstract

In this paper, 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 probability being applied by a particular user as a function of usage frequency and recency of the tag in the user’s past. This probability is further refined by considering the influence of the current semantic context of the user’s tagging situation. Using three real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike and Flickr, we show how refining frequency-based estimates by considering usage recency and contextual influence 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 simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as FolkRank, Pairwise Interaction Tensor Factorization and Collaborative Filtering. We conclude that our approach provides an accurate and computationally efficient model of a user’s temporal tagging behavior. We demonstrate how effective principles of recommender systems can be designed and implemented if human memory processes are taken into account.

Parts of this work have been included as an extended version in the article “Modeling Activation Processes in Human Memory to Predict the Reuse of Tags” submitted to the The Journal of Web Science.

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Notes

  1. 1.

    https://github.com/learning-layers/TagRec/.

  2. 2.

    http://www.kde.cs.uni-kassel.de/bibsonomy/dumps.

  3. 3.

    http://www.citeulike.org/faq/data.adp.

  4. 4.

    http://www.tagora-project.eu/.

  5. 5.

    \(F_1\)@\(5\) was also used as the main performance metric in the ECML PKDD Discovery Challenge 2009: http://www.kde.cs.uni-kassel.de/ws/dc09/.

  6. 6.

    http://www.kde.cs.uni-kassel.de/code.

  7. 7.

    http://www.informatik.uni-konstanz.de/rendle/software/tag-recommender/.

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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. The Know-Center is funded within the Austrian COMET Program - Competence Centers for Excellent Technologies - under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency (FFG).

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Authors and Affiliations

  1. Know-Center, Graz University of Technology, Graz, Austria

    Dominik Kowald

  2. Knowledge Technologies Institute, Graz University of Technology, Graz, Austria

    Dominik Kowald, Simone Kopeinik, Paul Seitlinger & Dietrich Albert

  3. Institute of Informatics, Tallin University, Tallinn, Estonia

    Tobias Ley

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Christoph Trattner

Authors
  1. Dominik Kowald
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  5. Dietrich Albert
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  6. Christoph Trattner
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Correspondence to Dominik Kowald .

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Editors and Affiliations

  1. University of Kassel, Kassel, Germany

    Martin Atzmueller

  2. Microsoft, Beijing, China

    Alvin Chin

  3. University of Kassel, Kassel, Germany

    Christoph Scholz

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Christoph Trattner

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Kowald, D., Kopeinik, S., Seitlinger, P., Ley, T., Albert, D., Trattner, C. (2015). Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context. 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_4

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