A Programming Interface and Platform Support for Developing Recommendation Algorithms on Large-Scale Social Networks

  • Alejandro Corbellini
  • Daniela Godoy
  • Cristian Mateos
  • Alejandro Zunino
  • Silvia Schiaffino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8658)


Friend recommendation algorithms in large-scale social networks such as Facebook or Twitter usually require the exploration of huge user graphs. In current solutions for parallelizing graph algorithms, the burden of dealing with distributed concerns falls on algorithm developers. In this paper, a simple yet powerful programming interface (API) to implement distributed graph traversal algorithms is presented. A case study on implementing a followee recommendation algorithm for Twitter using the API is described. This case study not only illustrates the simplicity offered by the API for developing algorithms, but also how different aspects of the distributed solutions can be treated and experimented without altering the algorithm code. Experiments evaluating the performance of different job scheduling strategies illustrate the flexibility or our approach.


Schedule Strategy Information Seeker Recommendation Algorithm Graph Database Adjacency Graph 
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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alejandro Corbellini
    • 1
  • Daniela Godoy
    • 1
  • Cristian Mateos
    • 1
  • Alejandro Zunino
    • 1
  • Silvia Schiaffino
    • 1
  1. 1.ISISTAN Research Institute - Consejo Nacional de Investigaciones Cientí́ficas y Técnicas (CONICET)Univ. Nacional del Centro de la Provincia de Bs. As. (UNICEN)TandilArgentina

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