In this paper we introduce the concept of preferential attachment in the context of recommendation and rating systems. We present several models incorporating different qualities that may manifest in such systems, such as inherent bias, and examine the resulting degree distributions (i.e. ratings) as snapshots and through time. We then take preliminary steps towards testing real-world feasibility with the Yelp Academic Dataset.
