  A variety of rating-based recommendation methods have been extensively
studied including the well-known collaborative filtering approaches and some
network diffusion-based methods, however, social trust relations are not
sufficiently considered when making recommendations. In this paper, we
contribute to the literature by proposing a trust-based recommendation method,
named CosRA+T, after integrating the information of trust relations into the
resource-redistribution process. Specifically, a tunable parameter is used to
scale the resources received by trusted users before the redistribution back to
the objects. Interestingly, we find an optimal scaling parameter for the
proposed CosRA+T method to achieve its best recommendation accuracy, and the
optimal value seems to be universal under several evaluation metrics across
different datasets. Moreover, results of extensive experiments on the two
real-world rating datasets with trust relations, Epinions and FriendFeed,
suggest that CosRA+T has a remarkable improvement in overall accuracy,
diversity, and novelty. Our work takes a step towards designing better
recommendation algorithms by employing multiple resources of social network
information.
