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

  • Dominik Kowald
  • Paul Seitlinger
  • Simone Kopeinik
  • Tobias Ley
  • Christoph Trattner
Conference paper

DOI: 10.1007/978-3-319-14723-9_5

Volume 8940 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
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. Lecture Notes in Computer Science, vol 8940. Springer, Cham

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 

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dominik Kowald
    • 1
    • 2
  • Paul Seitlinger
    • 2
  • Simone Kopeinik
    • 2
  • Tobias Ley
    • 3
  • 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