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Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender

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

Abstract

We assume that recommender systems are more successful, when they are based on a thorough understanding of how people process information. In the current paper we test this assumption in the context of social tagging systems. Cognitive research on how people assign tags has shown that they draw on two interconnected levels of knowledge in their memory: on a conceptual level of semantic fields or LDA topics, and on a lexical level that turns patterns on the semantic level into words. Another strand of tagging research reveals a strong impact of time-dependent forgetting on users’ tag choices, such that recently used tags have a higher probability being reused than “older” tags. In this paper, we align both strands by implementing a computational theory of human memory that integrates the two-level conception and the process of forgetting in form of a tag recommender. Furthermore, we test the approach in three large-scale social tagging datasets that are drawn from BibSonomy, CiteULike and Flickr.

As expected, our results reveal a selective effect of time: forgetting is much more pronounced on the lexical level of tags. Second, an extensive evaluation based on this observation shows that a tag recommender interconnecting the semantic and lexical level based on a theory of human categorization and integrating time-dependent forgetting on the lexical level results in high accuracy predictions and outperforms other well-established algorithms, such as Collaborative Filtering, Pairwise Interaction Tensor Factorization, FolkRank and two alternative time-dependent approaches. We conclude that tag recommenders will benefit from going beyond the manifest level of word co-occurrences, and from including forgetting processes on the lexical level.

Keywords

  • Personalized tag recommendations
  • Time-dependent recommender systems
  • Latent Dirichlet Allocation
  • LDA
  • Human categorization
  • Human memory model
  • BibSonomy
  • CiteULike
  • Flickr

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Fig. 1.
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Notes

  1. 1.

    We define a bookmark (also known as “post”) as the set of tags a target user has assigned to a target resource at a specific time, and the personomy as a collection of all bookmarks of a user.

  2. 2.

    http://mallet.cs.umass.edu/topics.php.

  3. 3.

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

  4. 4.

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

  5. 5.

    http://www.tagora-project.eu/data/#flickrphotos.

  6. 6.

    Note: We used the same dataset samples as in our previous work [9], except for CiteULike, where we used a smaller sample for reasons of computational effort in respect to the calculation of the LDA topics.

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

    NOTE: We also performed experiments with more than 1000 LDA topics (e.g., 2000, 3000, ...). However, as also shown by related work (e.g., [19, 24, 25]) this step did not help in increasing the performance of the LDA-based tag recommenders.

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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.

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Kowald, D., Seitlinger, P., Kopeinik, S., Ley, T., Trattner, C. (2015). Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender. 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_5

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