In the last few years, recommender systems have gained significant attention in the research community, due to the increasing availability of huge data collections, such as news archives, shopping catalogs, or virtual museums. In this scenario, there is a pressing need for applications to provide users with targeted suggestions to help them navigate this ocean of information. However, no much effort has yet been devoted to recommenders in the field of multimedia databases. In this paper, we propose a novel approach to recommendation in multimedia browsing systems, based on an importance ranking method that strongly resembles the well known PageRank ranking system. We model recommendation as a social choice problem, and propose a method that computes customized recommendations by originally combing intrinsic features of multimedia objects, past behavior of individual users and overall behavior of the entire community of users. We implemented a prototype of the proposed system and preliminary experiments have shown that our approach is promising. Copyright © 2010 ACM.

A ranking method for multimedia recommenders

Persia F.;
2010

Abstract

In the last few years, recommender systems have gained significant attention in the research community, due to the increasing availability of huge data collections, such as news archives, shopping catalogs, or virtual museums. In this scenario, there is a pressing need for applications to provide users with targeted suggestions to help them navigate this ocean of information. However, no much effort has yet been devoted to recommenders in the field of multimedia databases. In this paper, we propose a novel approach to recommendation in multimedia browsing systems, based on an importance ranking method that strongly resembles the well known PageRank ranking system. We model recommendation as a social choice problem, and propose a method that computes customized recommendations by originally combing intrinsic features of multimedia objects, past behavior of individual users and overall behavior of the entire community of users. We implemented a prototype of the proposed system and preliminary experiments have shown that our approach is promising. Copyright © 2010 ACM.
9781450301176
File in questo prodotto:
File Dimensione Formato  
civr2010.pdf

solo utenti autorizzati

Tipologia: Documento in Versione Editoriale
Licenza: Creative commons
Dimensione 341.76 kB
Formato Adobe PDF
341.76 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

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/11697/166112
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? ND
social impact