Workshop on Knowledge-Aware Recommender Systems

The workshop focuses on all the aspects related to the injection and adoption of knowledge sources in Recommender Systems. More and more precise and powerful recommendation algorithms and techniques have been proposed over the last years able to effectively assess users’ tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest for the recommendation engine. The aim of knowledge-aware recommender systems is to go beyond the traditional accuracy goal and to start a new generation of algorithms and approaches which exploit the knowledge encoded in ontological and logic-based knowledge bases, knowledge graphs as well as the semantics emerging from the analysis of semi-structured textual sources.

  • Vito Walter Anelli, Polytechnic University of Bari, Italy
  • Tommaso Di Noia, Polytechnic University of Bari, Italy
  • Pasquale Lops, University of Bari, Italy
  • Cataldo Musto, University of Bari, Italy
Steering Committee
  • Markus Zanker, Free University of Bozen, Italy


Sunday, Oct 7, 2018

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