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

arXiv: https://arxiv.org/abs/1606.08963 · HTML: https://mlsanity.com/paper/1606.08963/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [Label Ranking through Nonparametric Regression](https://mlsanity.com/paper/2111.02749/) (2021)

## Abstract (arXiv)

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.