Abstract

In the last years, hundreds of social networks sites have been launched with both professional (e.g., LinkedIn) and non-professional (e.g., MySpace, Facebook) orientations. This resulted in a renewed information overload problem, but it also provided a new and unforeseen way of gathering useful, accurate and constantly updated information about user interests and tastes. Content-based recommender systems can leverage the wealth of data emerging by social networks for building user profiles in which representations of the user interests are maintained. The idea proposed in this paper is to extract content-based user profiles from the data available in the LinkedIn social network, to have an image of the users' interests that can be used to recommend interesting academic research papers. A preliminary experiment provided interesting results which deserve further attention.

Autore Pugliese

• Musto Cataldo , Semeraro Giovanni , De Gemmis Marco , Lops Pasquale

Tutti gli autori

• MUSTO C.;NARDUCCI F.;SEMERARO G.;de GEMMIS M.;LOPS P.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2011

ISSN

Non Disponibile

ISBN

978-1-4503-0683-6

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

10

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile