Providing recommendations in social networks by integrating local and global reputation

We characterize our model by considering (i) the different relevance given to local and global reputation, (ii) the threshold that is used to consider a user unreliable and (iii) the dimension of the user's ego-network. Experiments performed on a real data set show that global reputation is useful only if the size of the user ego-network is small, as in the case of a newcomer. Moreover, the combined usage of global and local reputation leads to predict the expected trust with a high level of precision.