case of CF, clusters are created in order to reflect the platform’s needs on the background of the users’ (or items’) characteristics: applied to consumption forecasts, both behaviour predictions and item suggestions are “bent” by the use of Big Data, which allow the platform to modify the structure of the network through a continuous remodelling of the relevant clusters. Conversely, NS techniques allow the network structure to remain rigid, while data flow in a unique cluster (the entire network) and information stems from the measurement of connections in an immutable structure.

Network analysis and its properties – along with the data analysis made by any other recommendations system, although with a different computational effort – can be used by platforms in order to manage some of the factors influencing the purchase decisions by individuals plunged in the internet context[^10], as well as a range of other elements that come into play as regards such choices. In such sense, due to the high level of information asymmetry [@Akerlof1970; @tsai2011effect; @mavlanova2012signaling] associated to the deep consumers’ unawareness of how digital footprints remain on the web[^11], networks properties can be used to perform profiling and grouping practices, to the point that a seller 