Everyday video-sharing websites such as YouTube collect large amounts of new multimedia resources. Comments left by viewers often provide valuable information to describe sentiments, opinions and tastes of users. For this reason, we propose a novel re-ranking approach that takes into consideration that information in order to provide better recommendations of related videos. Early experiments indicate an improvement in the recommendation performance.

Galli, M., Gurini, D.F., Gasparetti, F., Micarelli, A., & Sansonetti, G. (2015). Analysis of User-generated Content for Improving YouTube Video Recommendation, 1441.

Analysis of User-generated Content for Improving YouTube Video Recommendation

GASPARETTI, FABIO;MICARELLI, Alessandro;SANSONETTI, GIUSEPPE
2015

Abstract

Everyday video-sharing websites such as YouTube collect large amounts of new multimedia resources. Comments left by viewers often provide valuable information to describe sentiments, opinions and tastes of users. For this reason, we propose a novel re-ranking approach that takes into consideration that information in order to provide better recommendations of related videos. Early experiments indicate an improvement in the recommendation performance.
Galli, M., Gurini, D.F., Gasparetti, F., Micarelli, A., & Sansonetti, G. (2015). Analysis of User-generated Content for Improving YouTube Video Recommendation, 1441.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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: http://hdl.handle.net/11590/298712
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact