  Awerbuch et al.'s approach to distributed recommender systems (DRSs) is to
have agents sample products at random while randomly querying one another for
the best item they have found; we improve upon this by adding a communication
network. Agents can only communicate with their immediate neighbors in the
network, but neighboring agents may or may not represent users with common
interests. We define two network structures: in the ``mailing-list model,''
agents representing similar users form cliques, while in the ``word-of-mouth
model'' the agents are distributed randomly in a scale-free network (SFN). In
both models, agents tell their neighbors about satisfactory products as they
are found. In the word-of-mouth model, knowledge of items propagates only
through interested agents, and the SFN parameters affect the system's
performance. We include a summary of our new results on the character and
parameters of random subgraphs of SFNs, in particular SFNs with power-law
degree distributions down to minimum degree 1. These networks are not as
resilient as Cohen et al. originally suggested. In the case of the widely-cited
``Internet resilience'' result, high failure rates actually lead to the
orphaning of half of the surviving nodes after 60% of the network has failed
and the complete disintegration of the network at 90%. We show that given an
appropriate network, the communication network reduces the number of sampled
items, the number of messages sent, and the amount of ``spam.'' We conclude
that in many cases DRSs will be useful for sharing information in a multi-agent
learning system.
