Proceedings of the 2nd Brazilian Workshop on Social Network Analysis and Mining (BrasNAM), XXXIII Congresso da Sociedade Brasileira de Computação - Supporting the efficient exploration of large-scale social networks for recommendation

Producción CyT

Proceedings of the 2nd Brazilian Workshop on Social Network Analysis and Mining (BrasNAM), XXXIII Congresso da Sociedade Brasileira de Computação - Supporting the efficient exploration of large-scale social networks for recommendation

Congreso

Autoría

CORBELLINI, A. ; MATEOS, C. ; GODOY, D. ; ZUNINO, A. ; SCHIAFFINO, S.

Fecha

2013

Editorial y Lugar de Edición

Brazilian Computing Society

Resumen Información suministrada por el agente en SIGEVA

Most recommendation algorithms in the context of large-scale social networks such as Twitter or Facebook struggle with the need of an efficient exploration of the huge and exponentially growing user graph. Current solutions in the form of graph-specific databases or frameworks for graph algorithms do not scale well for processing complex navigational patterns. In this paper we present an approach for supporting social recommendation algorithms that operate with large graphs in a computer cluste... Most recommendation algorithms in the context of large-scale social networks such as Twitter or Facebook struggle with the need of an efficient exploration of the huge and exponentially growing user graph. Current solutions in the form of graph-specific databases or frameworks for graph algorithms do not scale well for processing complex navigational patterns. In this paper we present an approach for supporting social recommendation algorithms that operate with large graphs in a computer cluster based on “policies”, rules that allow users to throttle the amount of parallelism and control task location. Experiments with a followee recommendation algorithm show the potentials of the proposed policies to solve recommendation problems in an efficient and scalable way.

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Palabras Clave

Social Recommender SystemsBig-dataDistributed SystemsSocial Networks