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The two most well-known classification and clustering algorithms-used by CF-are, respectively, k-nearest neighbour algorithm ( k-nn) (not to be confused with the “Nearest Neighbour Degree”, \(k_\), which origins from the network science field and which we will use later in this paper) and k-mean clustering (Sarwar et al. We tested our measures on Amazons’ instances, obtaining evidence which confirm the relevance of information extracted from nodes’ neighbourhood in order to steer targeted users. Our measure takes into account the quality of the connections among nodes as the one of a hypothetical user in relation to its neighbourhood, detecting how users with a good neighbourhood-hence of a superior connections set-obtain better information. We therefore propose a measure called Network Information Patrimony, considering the amount of information available within the system and we look into how platforms could exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages.
To this end, we rely on network science whose analytical tools allow data representations capable of highlighting relationships between subjects and/or items, extracting a great amount of information. In the present paper, we show how platforms’ performances still appear astonishing in relation to some unexplored data and networks properties, capable to enhance the platforms’ capacity to implement steering practices by means of an increased ability to estimate individuals’ preferences. Increased data gathering capacity, together with the spread of data analytics techniques, has prompterd an unprecedented concentration of information related to the individuals’ preferences in the hands of a few gatekeepers.
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