  In modern ranking problems, different and disparate representations of the
items to be ranked are often available. It is sensible, then, to try to combine
these representations to improve ranking. Indeed, learning to rank via
combining representations is both principled and practical for learning a
ranking function for a particular query. In extremely data-scarce settings,
however, the amount of labeled data available for a particular query can lead
to a highly variable and ineffective ranking function. One way to mitigate the
effect of the small amount of data is to leverage information from semantically
similar queries. Indeed, as we demonstrate in simulation settings and real data
examples, when semantically similar queries are available it is possible to
gainfully use them when ranking with respect to a particular query. We describe
and explore this phenomenon in the context of the bias-variance trade off and
apply it to the data-scarce settings of a Bing navigational graph and the
Drosophila larva connectome.
