  Many algorithms for ranked data become computationally intractable as the
number of objects grows due to the complex geometric structure induced by
rankings. An additional challenge is posed by partial rankings, i.e. rankings
in which the preference is only known for a subset of all objects. For these
reasons, state-of-the-art methods cannot scale to real-world applications, such
as recommender systems. We address this challenge by exploiting the geometric
structure of ranked data and additional available information about the objects
to derive a kernel for ranking based on the graph cut function. The graph cut
kernel combines the efficiency of submodular optimization with the theoretical
properties of kernel-based methods. The graph cut kernel combines the
efficiency of submodular optimization with the theoretical properties of
kernel-based methods.
