  Recommendation plays a key role in e-commerce and in the entertainment
industry. We propose to consider successive recommendations to users under the
form of graphs of recommendations. We give models for this representation.
Motivated by the growing interest for algorithmic transparency, we then propose
a first application for those graphs, that is the potential detection of
introduced recommendation bias by the service provider. This application relies
on the analysis of the topology of the extracted graph for a given user; we
propose a notion of recommendation coherence with regards to the topological
proximity of recommended items (under the measure of items' k-closest
neighbors, reminding the "small-world" model by Watts & Stroggatz). We finally
illustrate this approach on a model and on Youtube crawls, targeting the
prediction of "Recommended for you" links (i.e., biased or not by Youtube).
