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Publication Detail
Graph-based recommendation system
  • Publication Type:
  • Authors:
    Yang K, Toni L
  • Publisher:
  • Publication date:
  • Pagination:
    798, 802
  • Published proceedings:
    2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
  • ISBN-13:
  • Status:
  • Name of conference:
    2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
  • Conference place:
    Anaheim, California, USA
  • Conference start date:
  • Conference finish date:
© 2018 IEEE. In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the geometry of the user space to create meaningful clusters in the user domain. This reduces the dimensionality of the recommendation problem while preserving the accuracy of MAB. We then study the effect of graph sparsity and clusters size on the MAB performance and provide exhaustive simulation results both in synthetic and in real-case datasets. Simulation results show improvements with respect to state-of-the-art MAB algorithms.
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