Modeling Activation Processes in Human Memory to Predict the Reuse of Tags

Authors

  • Christoph Trattner Know-Center, Graz University of Technology
  • Dominik Kowald Know-Center, Graz University of Technology
  • Paul Seitlinger KTI, Graz University of Technology
  • Simone Kopeinik KTI, Graz University of Technology
  • Tobias Ley Institute of Informatics, Tallin University

DOI:

https://doi.org/10.1561/106.00000004

Abstract

Several successful tag recommendation mechanisms have been developed, including algorithms built upon Collaborative Filtering, Tensor Factorization, graph-based and simple "most popular tags" approaches. From an economic perspective, the latter approach has been convincing since calculating frequencies is computationally efficient and effective with respect to different recommender evaluation metrics. In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory in order to extend these conventional "most popular tags" approaches. 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 simple 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.

Author Biographies

  • Christoph Trattner, Know-Center, Graz University of Technology

    Dr. Christoph Trattner is currently working as a post-doctoral researcher and the head of the Social
    Computing Research Group at Know-Center, Austria’s competence center for knowledge technologies.
    He has a PhD (with hons), a MSc (with hons) and BSc in Computer Science and Telematics from
    Graz University of Technology (Austria). His research interests include social computing, social systems,
    social media, social networks, social search & navigation, human computer interaction, data mining,
    user proï¬ling, adaptation, personalization, recommender systems and machine learning. He was involved,
    either as a collaborator or a project leader, in various national and international EU-funded research
    projects that dealt with social semantic technologies. Currently, he is the work package leader for the
    development of the social semantic server for the LEARNING LAYERS project funded by the EU.
    During the last four years, he published a signiï¬cant number of scientiï¬c articles in top venues and
    journals, e.g., the Journal of the Association for Information Science and Technology (JASIST), the
    ACM World Wide Web Conference (WWW), the IEEE Conference on Social Computing (SocialCom),
    the ACM WebScience Conference (WebSci), the ACM/IEEE International Conference on Advances in
    Social Networks Analysis and Mining (ASONAM), the ACM International Conference on Information
    and Knowledge Management (CIKM), the ACM Conference on Computer Supported Cooperative Work
    (CSCW) and the ACM Conference on Hypertext and Social Media (HT). He is the winner of several Best
    Paper/Poster Awards and Nominations. He regularly acts as a PC member on several top-tier conferences
    and co-organizes or co-chaires a number of workshops and conferences.

  • Dominik Kowald, Know-Center, Graz University of Technology
    Dominik Kowald is a university assistant at Knowledge Technologies Institute (Graz University of Technology) and software developer at the Know-Center. He studied Software-Engineering and Economy at Graz University of Technology and recently started his PhD on the topic: “Recommending Artifacts in Social-Semantic Networksâ€. In this respect his main research goal is to examine how recommender systems can be enhanced using models coming from human cognition, especially when dealing with tag and time information. Moreover, he is interested in the development of flexible recommender frameworks (e.g., TagRec). Currently he is working in the EU-IP Learning Layers, which is about scaling of technologies for informal learning in SME clusters. Furthermore, he has worked in the EU-project Organic Lingua, which is about demonstrating the potential of multilingual Web portal for sustainable agricultural & environmental education. Additionally he is working as a teaching assistant for the course Web Science and Web Technologies and as a software developer in different COMET-funded projects.

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Published

2016-02-18

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Vol. 2 (2016)

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