Graph-Based Learning on Sparse Data for Recommendation Systems in Social Networks

  • J. David Nuñez-GonzalezEmail author
  • Manuel Graña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9108)


Graph-based supervised learning is a useful tool to model data supported by powerful algebraic techniques. This paper proposes a novel approach using Graph-based supervised learning to handle the problem of building Recommendation Systems in Social Networks. Specifically, the main contribution of this paper is to propose two ways to construct Recomendations Systems based on Colaborative Filtering. The first way consists in building a feature matrix from the Web of Trust of each user. The second way builds a feature matrix based on similiraties among users according to the criterion of the system without taking into consideration the Web of Trust of the target user.


Recommendation System Support Vector Regression Sparse Representation Sparse Data Sparse Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computational Intellligence Group of the University of the Basque Country (UPV/EHU)VizcayaSpain
  2. 2.ENGINE project at the Wroclaw University of Tecnology (WrUT)WroclawPoland

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