  Many collaborative recommender systems leverage social correlation theories
to improve suggestion performance. However, they focus on explicit relations
between users and they leave out other types of information that can contribute
to determine users' global reputation; e.g., public recognition of reviewers'
quality. We are interested in understanding if and when these additional types
of feedback improve Top-N recommendation. For this purpose, we propose a
multi-faceted trust model to integrate local trust, represented by social
links, with various types of global trust evidence provided by social networks.
We aim at identifying general classes of data in order to make our model
applicable to different case studies. Then, we test the model by applying it to
a variant of User-to-User Collaborative filtering (U2UCF) which supports the
fusion of rating similarity, local trust derived from social relations, and
multi-faceted reputation for rating prediction. We test our model on two
datasets: the Yelp one publishes generic friend relations between users but
provides different types of trust feedback, including user profile
endorsements. The LibraryThing dataset offers fewer types of feedback but it
provides more selective friend relations aimed at content sharing. The results
of our experiments show that, on the Yelp dataset, our model outperforms both
U2UCF and state-of-the-art trust-based recommenders that only use rating
similarity and social relations. Differently, in the LibraryThing dataset, the
combination of social relations and rating similarity achieves the best
results. The lesson we learn is that multi-faceted trust can be a valuable type
of information for recommendation. However, before using it in an application
domain, an analysis of the type and amount of available trust evidence has to
be done to assess its real impact on recommendation performance.
