evolution of clustering-based profiling techniques and related limitations {#recsystems}
==============================================================================

Since the mid-90s e-commerce platforms begun exploiting input provided by customers in order to generate a list of items to be recommended to them [@Kamishima2011]. A technical way through which platforms improve their own algorithms in order to identify individual preferences is represented by [*Collaborative Filtering*]{} techniques ($CF$)[^2]. The aim of platforms is indeed to determine the preferences of individuals not only on the basis of data produced by some targeted ones, but also by using information derived by users related to him by the same purchase choices and preferences. In this regard, already back in 2010, in a special issue of The Economist, reference was made to the fact that Amazon and Netflix were using a statistical technique called [*collaborative filtering*]{} in order to make recommendations to users based on what other users like [@TheEconomist2010]. Algorithms aggregate items from similar customers, then eliminate items that a user has already purchased, recommending the remaining items to the same subject[^3].

By profiling consumers through their digital footprints[^4], firms can predict individuals’ demand as well as their price point sensitivity, ultimately altering the balance of power in their price and value negotiations 