In an era of information explosion, recommendation systems play an important
role in people's daily life by facilitating content exploration. It is known
that user activeness, i.e., number of behaviors, tends to follow a long-tail
distribution, where the majority of users are with low activeness. In practice,
we observe that tail users suffer from significantly lower-quality
recommendation than the head users after joint training. We further identify
that a model trained on tail users separately still achieve inferior results
due to limited data. Though long-tail distributions are ubiquitous in
recommendation systems, improving the recommendation performance on the tail
users still remains challenge in both research and industry. Directly applying
related methods on long-tail distribution might be at risk of hurting the
experience of head users, which is less affordable since a small portion of
head users with high activeness contribute a considerate portion of platform
revenue. In this paper, we propose a novel approach that significantly improves
the recommendation performance of the tail users while achieving at least
comparable performance for the head users over the base model. The essence of
this approach is a novel Gradient Aggregation technique that learns common
knowledge shared by all users into a backbone model, followed by separate
plugin prediction networks for the head users and the tail users
personalization. As for common knowledge learning, we leverage the backward
adjustment from the causality theory for deconfounding the gradient estimation
and thus shielding off the backbone training from the confounder, i.e., user
activeness. We conduct extensive experiments on two public recommendation
benchmark datasets and a large-scale industrial datasets collected from the
Alipay platform. Empirical studies validate the rationality and effectiveness
of our approach.

该论文提出了一种新方法，通过梯度聚合技术，学习所有用户的共同知识并在此基础上，为头部用户和尾部用户分别提供定制化的预测，从而显著提高了尾部用户的推荐效果。