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 associations. Our experimental results indicate that our method of trust inferences significantly improves the quality performance of the classic CF method.