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We consider the problem of personalization of online services from theviewpoint of ad targeting, where we seek to find the best ad categories to beshown to each user, resulting in improved user experience and increasedadvertisers' revenue. We propose to address this problem as a task of rankingthe ad categories depending on a user's preference, and introduce a novel labelranking approach capable of efficiently learning non-linear, highly accuratemodels in large-scale settings. Experiments on a real-world advertising dataset with more than 3.2 million users show that the proposed algorithmoutperforms the existing solutions in terms of both rank loss and top-Kretrieval performance, strongly suggesting the benefit of using the proposedmodel on large-scale ranking problems.

Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests