ISISTAN   23985
INSTITUTO SUPERIOR DE INGENIERIA DEL SOFTWARE
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Supporting the Efficient Exploration of Large-scale Social Networks for Recommendation
Autor/es:
ALEJANDRO CORBELLINI; CRISTIAN MATEOS; DANIELA GODOY; ALEJANDRO ZUNINO; SILVIA SCHIAFFINO
Lugar:
Maceió
Reunión:
Workshop; II Brazilian Workshop on Social Network Analysis and Mining (BraSNAM); 2013
Institución organizadora:
Universidade Federal de Minas Gerais, Universidade Federal do Rio de Janeiro
Resumen:
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.