Ontology based recommender system using social network data

Mohamad Arafeh, Paolo Ceravolo, Azzam Mourad, Ernesto Damiani, Emanuele Bellini

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis.

Original languageBritish English
Pages (from-to)769-779
Number of pages11
JournalFuture Generation Computer Systems
Volume115
DOIs
StatePublished - Feb 2021

Keywords

  • Big data
  • Data analysis
  • Data miner
  • Data sampling
  • Ontology
  • Recommender system
  • Social network

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