The similarity between two data objects of the same type (e.g., two resumes,
two job descriptions, etc.) is determined using predictive modeling. A basic assumption
is that training datasets are available containing compatibility measures between
objects of the first type and data objects of a second type, but that training
datasets measuring similarity between objects of the first type are not. A first
predictive model is trained to assess compatibility between data objects of a first
type and data objects of a second type. Then, in one scenario, pairs of objects
of the first type are compared for similarity by running them through the first
predictive model as if one object of the pair is an object of the first type and
the other object of the pair is an object of the second type. Alternatively, for
each object in a set of objects of the first type, the first predictive model is
used to create a respective vector of compatibility scores against a fixed set
of objects of the second type; these various vectors are then used to derive measures
of similarity between pairs of objects of the first type, from which a second predictive
model is trained, and the second predictive model is then used to assess the similarity
of pairs of objects of the first type.
