Abstract

Collaborative Filtering (CF) aims at predicting the user interest for a given item. In CF systems a set of users ratings is used to predict the rating of a given user on a given item using the ratings of a set of users who have already rated the item and whose preferences are similar to those of the user. In this paper we propose to use a framework based on uncertain graphs in order to deal with collaborative filtering problems. In this framework relationships among users and items and their corresponding likelihood will be encoded in a uncertain graph that can then be used to infer the probability of existence of a link between an user and an item involved in the graph. In order to solve CF tasks the framework uses an approximate inference method adopting a constrained simple path query language. The aim of the paper is to verify whether uncertain graphs are a valuable tool for CF, by solving classical, complex and structured problems. The performance of the proposed approach is reported when applied to a real-world domain.

Autore Pugliese

• Di Mauro Nicola

Tutti gli autori

• ESPOSITO F.;DI MAURO N.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2012

ISSN

1613-0073

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

1

Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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