SERUM: Collecting Semantic User Behavior for Improved News Recommendations

  • Till Plumbaum
  • Andreas Lommatzsch
  • Ernesto William De Luca
  • Sahin Albayrak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7138)

Abstract

How can semantic data and semantic technologies be leveraged for personalization and recommendation services? In this paper, we present SERUM (Semantic Recommendations based on large unstructured datasets), a news recommendation system that utilizes semantic technologies to collect implicit user behavior and to build semantic user models. These models, combined with large-scale semantic datasets, are then used to compute personalized news recommendations using graph-based algorithms. We introduce the building blocks of SERUM for the semantic data management, personalization and recommendation, with the main focus on the implicit user behavior collection. Therefore, our system uses RDFa to collect meaningful user behavior and a self-developed user behavior ontology (the User Behavior Ontology, in short UBO) to build semantic user behavior models. The main contribution of this work is the introduction of the UBO and the associated semantic user tracking and modeling process.

Keywords

user behavior semantic recommendation graph based recommender news personalization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Till Plumbaum
    • 1
  • Andreas Lommatzsch
    • 1
  • Ernesto William De Luca
    • 1
  • Sahin Albayrak
    • 1
  1. 1.DAI LaborBerlin Institute of TechnologyBerlinGermany

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