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. 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. 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. 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. 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 tested our measures on Amazons’ instances, obtaining evidence which confirm the relevance of information extracted from nodes’ neighbourhood in order to steer targeted users.

数据收集能力的提高以及数据分析技术的普及，促使与个人偏好有关的信息空前集中在少数几个看门人手中。在本文中，我们展示了平台的性能相对于某些未探究的数据和网络属性如何仍然令人惊讶，能够通过提高估计个人偏好的能力来增强平台实施指导实践的能力。为此，我们依靠网络科学，其分析工具允许数据表示能够突出显示主题和/或项目之间的关系，并提取大量信息。因此，我们提出了一种称为“网络信息保护”的措施，考虑到系统内可用的信息量，我们研究了平台如何利用网络中连接的配置文件产生的数据，以期获得竞争优势。我们的措施考虑到了作为其邻居的假设用户之一的节点之间的连接质量，从而检测了具有良好邻居的用户（因此拥有优越的连接集）如何获得更好的信息。我们在亚马逊实例上测试了我们的措施，获得了证据，这些证据证实了从节点附近提取的信息的相关性，从而可以引导目标用户。我们的措施考虑到了作为其邻居的假设用户之一的节点之间的连接质量，从而检测了具有良好邻居的用户（因此拥有优越的连接集）如何获得更好的信息。我们在亚马逊实例上测试了我们的措施，获得了证据，这些证据证实了从节点附近提取的信息的相关性，从而可以引导目标用户。我们的措施考虑到了作为其邻居的假设用户之一的节点之间的连接质量，从而检测了具有良好邻居的用户（因此拥有优越的连接集）如何获得更好的信息。我们在亚马逊实例上测试了我们的措施，获得了证据，这些证据证实了从节点附近提取的信息的相关性，从而可以引导目标用户。