Consumers can nowadays relay on the support of specific recommendation agents designed to reduce search costs and increase the chance to find products and services that match their needs and preferences. Typical instances of how recommender agents are placed on e-commerce platforms are represented by statements like "you may also like..." or " People who like this also like" that online buyers typically encounter after having completed a purchase. Although these agents are widely adopted in online shopping contexts it remains a topic that is largely not researched in the marketing perspective. Their implicit social structure enabled by the algorithms, the role of individuals within the implicit networks and their role have not been investigated yet. We propose a Network Analysis on Amazon's public dataset (>10Gb) with the aim to investigate implicit network of consumption created by algorithms and their structure.
To the the extent of our knowledge, this study provide a sociological perspective of Recommendation Algorithms and an understanding of the existing implicit communities and their structure through a Network Analysis. The Network Analysis will be carried out on large public datasets provided by Amazon with cloud computing technologies since the size exceed 10Gb.