Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests
This addresses the problem of improving ad targeting for online services to enhance user experience and increase advertiser revenue, representing an incremental advance in large-scale ranking methods.
The paper tackles the problem of personalizing online ad targeting by ranking ad categories based on user preferences, introducing a novel label ranking approach that learns non-linear models efficiently at scale. Experiments on real-world advertising data with over 3.2 million users show the algorithm outperforms existing solutions in rank loss and top-K retrieval performance.
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.