Social content matching in mapreduce (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• Social content matching in mapreduce (Articolo in rivista) (literal)

Anno

• 2011-01-01T00:00:00+01:00 (literal)

Alternative label

• Gionis, Aristides (3); Sozio, Mauro (2); De Francisci Morales, Gianmarco (1) (2011)
  Social content matching in mapreduce
  in Proceedings of the VLDB Endowment; ACM, Association for computing machinery, New York (Stati Uniti d'America)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Gionis, Aristides (3); Sozio, Mauro (2); De Francisci Morales, Gianmarco (1) (literal)

Pagina inizio

• 460 (literal)

Pagina fine

• 469 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

• EU Project: ASSETS: Advanced Search Services and Enhanced Technological Solutions for the European Digital Library, Grant Agreement 250527. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.vldb.org/pvldb/vol4/p460-morales.pdf (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 4 (literal)

Rivista

• Proceedings of the VLDB Endowment (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

• 10 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 7 (literal)

Note

• Scopu (literal)
• PuMa (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• (1) CNR-ISTI, Pisa; (2) Max-Planck-Institut für Informatik, Saarbrücken; (3) Yahoo! Research, Barcelona, Spain (literal)

Titolo

• Social content matching in mapreduce (literal)

Abstract

• Matching problems are ubiquitous. They occur in economic markets, labor markets, internet advertising, and elsewhere. In this paper we focus on an application of matching for social media. Our goal is to distribute content from information suppliers to information consumers. We seek to maximize the overall relevance of the matched content from suppliers to consumers while regulating the overall activity, e.g., ensuring that no consumer is overwhelmed with data and that all suppliers have chances to deliver their content. We propose two matching algorithms, GreedyMR and StackMR, geared for the MapReduce paradigm. Both algorithms have provable approximation guarantees, and in practice they produce high-quality solutions. While both algorithms scale extremely well, we can show that StackMR requires only a poly-logarithmic number of MapReduce steps, making it an attractive option for applications with very large datasets. We experimentally show the trade-offs between quality and efficiency of our solutions on two large datasets coming from real-world social-media web sites. (literal)

Editore

• ACM, Association for computing machinery (Editore)

Prodotto di

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Sistemi e algoritmi per Big Data (ICT.P09.006.001) (Modulo)

Autore CNR

• Gianmarco De Francisci Morales (Persona)

Insieme di parole chiave

• Parole chiave di "Social content matching in mapreduce" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Sistemi e algoritmi per Big Data (ICT.P09.006.001) (Modulo)

Autore CNR di

• Gianmarco De Francisci Morales (Persona)

Editore di

• ACM, Association for computing machinery (Editore)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Proceedings of the VLDB Endowment (Rivista)

Insieme di parole chiave di

• Parole chiave di "Social content matching in mapreduce" (Insieme di parole chiave)