  We present an analysis of a person-to-person recommendation network,
consisting of 4 million people who made 16 million recommendations on half a
million products. We observe the propagation of recommendations and the cascade
sizes, which we explain by a simple stochastic model. We analyze how user
behavior varies within user communities defined by a recommendation network.
Product purchases follow a 'long tail' where a significant share of purchases
belongs to rarely sold items. We establish how the recommendation network grows
over time and how effective it is from the viewpoint of the sender and receiver
of the recommendations. While on average recommendations are not very effective
at inducing purchases and do not spread very far, we present a model that
successfully identifies communities, product and pricing categories for which
viral marketing seems to be very effective.
