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

  • Dominik Kowald
  • Simone Kopeinik
  • Paul Seitlinger
  • Tobias Ley
  • Dietrich Albert
  • Christoph Trattner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Personalized tag recommendations Time-dependent recommender systems Base-level learning equation ACT-R Human memory model BibSonomy CiteULike Flickr 

Notes

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dominik Kowald
    • 1
    • 2
  • Simone Kopeinik
    • 2
  • Paul Seitlinger
    • 2
  • Tobias Ley
    • 3
  • Dietrich Albert
    • 2
  • Christoph Trattner
    • 4
  1. 1.Know-CenterGraz University of TechnologyGrazAustria
  2. 2.Knowledge Technologies InstituteGraz University of TechnologyGrazAustria
  3. 3.Institute of InformaticsTallin UniversityTallinnEstonia
  4. 4.Norwegian University of Science and TechnologyTrondheimNorway

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