Knowledge Graph For Data Mining

KGDM: Problem Statement

  • Matrix factorisation based recommendation systems have some limitations:                          
    • The performance of such recommendation systems is inversely proportional to the
      sparsity of user item matrix.
    • Item latency or cold start for newly added
      items.
    • User cold start.
  • A solution to deal with the shortcomings of Matrix Factorisation based recommendation systems is to use items’ (and users’) metadata and develop algorithms which can exploit this newly added data.
  • Generally, this data is connected and can be
    expressed as Knowledge Graphs.
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Technologies Used