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EnglishCollaborative 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.
Uncertain Graphs meet Collaborative Filtering / Claudio Taranto; Di Mauro N; Floriana Esposito. - 835(2012), pp. 89-100. ((Intervento presentato al convegno IIR 2012 Workshop 26-27 january 2012.
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| Titolo: | Uncertain Graphs meet Collaborative Filtering | |
| Autori: | ||
| Data di pubblicazione: | 2012 | |
| Rivista: | ||
| Citazione: | Uncertain Graphs meet Collaborative Filtering / Claudio Taranto; Di Mauro N; Floriana Esposito. - 835(2012), pp. 89-100. ((Intervento presentato al convegno IIR 2012 Workshop 26-27 january 2012. | |
| 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. | |
| Handle: | http://hdl.handle.net/11586/31160 | |
| Appare nelle tipologie: | 2.1 Contributo in volume (Capitolo o Saggio) |
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