  We present a novel framework for evaluating recommendation algorithms in
terms of the `jumps' that they make to connect people to artifacts. This
approach emphasizes reachability via an algorithm within the implicit graph
structure underlying a recommender dataset, and serves as a complement to
evaluation in terms of predictive accuracy. The framework allows us to consider
questions relating algorithmic parameters to properties of the datasets. For
instance, given a particular algorithm `jump,' what is the average path length
from a person to an artifact? Or, what choices of minimum ratings and jumps
maintain a connected graph? We illustrate the approach with a common jump
called the `hammock' using movie recommender datasets.
