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Auflistung nach Autor:in "Woerndl, Wolfgang"

1 - 3 von 3
  • Konferenzbeitrag
    ABIS 2014 - 20th International Workshop on Adaptivity and User Modeling
    (Mensch & Computer 2014 - Workshopband, 2014) Augstein, Mirjam; Brandherm, Boris; Herder, Eelco; Heckmann, Dominikus; Woerndl, Wolfgang
    ABIS 2014 is an international workshop, organized by the SIG on Adaptivity and User Modeling of the German Gesellschaft für Informatik . For the last 19 years, the ABIS Workshop has been a highly interactive forum for discussing the state of the art in personalization and user modeling. Latest developments in industry and research are presented in plenary sessions, forums, and tutorials. Researchers, Ph.D. students and Web professionals obtain and exchange novel ideas, expertise and feedback on ongoing research before submitting their work to major conferences such as CHI, UMAP, WWW and SIGIR.
  • Konferenzbeitrag
    Influencing Factors for User Context in Proactive Mobile Recommenders
    (Mensch & Computer 2012 – Workshopband: interaktiv informiert – allgegenwärtig und allumfassend!?, 2012) Woerndl, Wolfgang; Lerchenmueller, Benjamin; Schulze, Florian
    Proactive recommender systems break the standard request-response pattern of traditional recommenders by pushing item suggestions to the user when the situation seems appropriate. To support proactive recommendations in a mobile scenario, we have developed a two-phase proactivity model based on the current context of the user. In this paper, we explain our approach to model context by identifying different components: user and device status, and user activity. We have conducted an online survey among over 100 users to investigate how different context attributes influence the decision when to generate proactive recommendations. Thus, we were able to acquire appropriateness factors and weights for the context features in our proactivity model.
  • Konferenzbeitrag
    Influencing Factors for User Context in Proactive Mobile Recommenders
    (ABIS 2012, 2012) Woerndl, Wolfgang; Lerchenmueller, Benjamin; Schulze, Florian
    Proactive recommender systems break the standard request-response pattern of traditional recommenders by pushing item suggestions to the user when the situation seems appropriate. To support proactive recommendations in a mobile scenario, we have developed a two-phase proactivity model based on the current context of the user. In this paper, we explain our approach to model context by identifying different components: user and device status, and user activity. We have conducted an online survey among over 100 users to investigate how different context attributes influence the decision when to generate proactive recommendations. Thus, we were able to acquire appropriateness factors and weights for the context features in our proactivity model.