@lam2010system].

Although some authors have tried to sort out such matters via combining data form social network websites (ie information gathered via their explicit social network) and recommendation systems [@konstas2009social; @liu2010use], the aforementioned problems still remain unsolved [@castillejo2012social].

Network properties applied to profiling tecniques {#networkprop}
=================================================

As shown by Google search engine, networks’ properties might be crucial in resolving recommendation systems limitations [@page1999pagerank; @castillejo2012social]. Indeed, platforms may rely on an alternative data representation – that is, the network representation of the relationships between nodes representing items offered by the platforms and/or users – based on the [*network science*]{} (NS) paradigm [@barabasi2013network]. As matter of fact, a formal network representation (i.e. a mathematical representation) allows to organise users or objects as connected via links that make explicit predefined relationships. It is therefore possible to use network science tools and, in particular, those inherited from [*Social Network Analysis[^8]*]{} [e.g. @scott2011sage] to build a scheme describing preferences’ patterns featuring connections between items and/or users. According to the literature, such representation has become the preferential tool used to study complex systems [@wang2003complex].

The use of Network Science may improve the quality of recommendations made to consumers via the combination of metadata stemming from the 