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ITRUST
2005
Springer

Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences

11 years 11 months ago
Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences
Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as “similar” according to their logged history of prior transactions. However, the applicability of CF is limited due to the sparsity problem, which refers to a situation that transactional data are lacking or are insufficient. In an attempt to provide high-quality recommendations even when data are sparse, we propose a method for alleviating sparsity using trust inferences. Trust inferences are transitive associations between users in the context of an underlying social network and are valuable sources of additional information that help dealing with the sparsity and the cold-start problems. A trust computational model has been developed that permits to define the subjective notion of trust by applying confidence and uncertainty properties to network associations. We compare our method with the classic CF that does not consider any transitive ...
Manos Papagelis, Dimitris Plexousakis, Themistokli
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where ITRUST
Authors Manos Papagelis, Dimitris Plexousakis, Themistoklis Kutsuras
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