We design and study recommendation algorithms for a fully decentralized scenario in which each item/node of a network recommends other items/nodes only on the basis of simple statistics on the behaviour of users that visited the node in the past. We perform a theoretical and experimental study assessing that very simple heuristics can provide recommendations of good quality even in such a restrictive scenario. © 2008 IEEE.
Self-adaptive recommendation systems: Models and experimental analysis / Becchetti, Luca; Colesanti, Ugo Maria; MARCHETTI SPACCAMELA, Alberto; Vitaletti, Andrea. - (2008), pp. 479-480. (Intervento presentato al convegno 2nd IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2008 tenutosi a Venice; Italy nel 20 October 2008 through 24 October 2008) [10.1109/saso.2008.55].
Self-adaptive recommendation systems: Models and experimental analysis
BECCHETTI, Luca;COLESANTI, Ugo Maria;MARCHETTI SPACCAMELA, Alberto;VITALETTI, Andrea
2008
Abstract
We design and study recommendation algorithms for a fully decentralized scenario in which each item/node of a network recommends other items/nodes only on the basis of simple statistics on the behaviour of users that visited the node in the past. We perform a theoretical and experimental study assessing that very simple heuristics can provide recommendations of good quality even in such a restrictive scenario. © 2008 IEEE.| File | Dimensione | Formato | |
|---|---|---|---|
|
VE_2008_11573-367292.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
168.81 kB
Formato
Adobe PDF
|
168.81 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
