  We consider the problem of personalization of online services from the
viewpoint of ad targeting, where we seek to find the best ad categories to be
shown to each user, resulting in improved user experience and increased
advertisers' revenue. We propose to address this problem as a task of ranking
the ad categories depending on a user's preference, and introduce a novel label
ranking approach capable of efficiently learning non-linear, highly accurate
models in large-scale settings. Experiments on a real-world advertising data
set with more than 3.2 million users show that the proposed algorithm
outperforms the existing solutions in terms of both rank loss and top-K
retrieval performance, strongly suggesting the benefit of using the proposed
model on large-scale ranking problems.
