There are different types of information systems, such as those that perform group recommendations and market segmentations, which operate with groups of users. In order to combine the individual preferences and properly address suggestions to users, group modeling strategies are employed. Nowadays, data is characterized by large amounts in terms of volume, speed, and variety (the so-called big data issue). In this paper, we are going to tackle the problem of modeling group preferences in big data scenarios. This study will present the existing strategies, and we are going to present criteria to design the algorithms that implement them when big amounts of data have to be combined. Moreover, a set of best practices discusses under which conditions the presented strategies can be adopted in big data scenarios.

Design criteria to model groups in big data scenarios: algorithms and best practices

BORATTO, LUDOVICO;FENU, GIANNI;PAU, PIER LUIGI
2015-01-01

Abstract

There are different types of information systems, such as those that perform group recommendations and market segmentations, which operate with groups of users. In order to combine the individual preferences and properly address suggestions to users, group modeling strategies are employed. Nowadays, data is characterized by large amounts in terms of volume, speed, and variety (the so-called big data issue). In this paper, we are going to tackle the problem of modeling group preferences in big data scenarios. This study will present the existing strategies, and we are going to present criteria to design the algorithms that implement them when big amounts of data have to be combined. Moreover, a set of best practices discusses under which conditions the presented strategies can be adopted in big data scenarios.
Group modeling; Big data; Algorithms; Design
File in questo prodotto:
File Dimensione Formato  
KDWEB_2015_boratto_fenu_pau.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: versione editoriale
Dimensione 218.71 kB
Formato Adobe PDF
218.71 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/139296
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact