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

TL;DR

This paper introduces a novel non-linear label ranking method tailored for large-scale user interest prediction, significantly improving ad targeting accuracy and efficiency in real-world applications with millions of users.

Contribution

The paper presents a new scalable label ranking approach that effectively models non-linear user preferences for large-scale ad targeting tasks.

Findings

Outperforms existing solutions in rank loss

Achieves higher top-K retrieval performance

Effective on datasets with over 3.2 million users

Abstract

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.