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  4. Preference ontologies based on Social Media for compensating the Cold Start Problem
 
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2014
Konferenzbeitrag
Titel

Preference ontologies based on Social Media for compensating the Cold Start Problem

Abstract
Recommendation systems leverage future internet services to predict personalized recommendations for products, services, media entities or other offerings. Based on the research and development of the FIcontent 2 initiative, we introduce an approach to compensate Cold Start and Sparsity Problems by analyzing semantics of external textual data, in terms of comments from social networks as well as item reviews from product and rating services. Thereby sentiment analysis and semantic keyword extraction approaches are explained and evaluated by using preliminary implementations. The mined data is transferred into, so called, preference ontologies describing the users interest in automatic analyzed topics and subsequently mapped to the properties of items in order to calculate the associated recommendation value.
Author(s)
Krauss, Christopher
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Braun, Sascha
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Arbanowski, Stefan
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Hauptwerk
8th Workshop on Social Network Mining and Analysis for Business, Consumer and Social Insighths, SNA KDD 2014. Proceedings
Konferenz
Workshop on Social Network Mining and Analysis for Business, Consumer and Social Insighths (SNA KDD) 2014
International Conference on Knowledge Discovery and Data Mining (KDD) 2014
Thumbnail Image
DOI
10.1145/2659480.2659504
Language
Englisch
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FOKUS
Tags
  • recommendation engine...

  • preference ontology

  • cold start

  • sparsity

  • semantic keyword extr...

  • sentiment analysis

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