  Sequential user behavior modeling plays a crucial role in online
user-oriented services, such as product purchasing, news feed consumption, and
online advertising. The performance of sequential modeling heavily depends on
the scale and quality of historical behaviors. However, the number of user
behaviors inherently follows a long-tailed distribution, which has been seldom
explored. In this work, we argue that focusing on tail users could bring more
benefits and address the long tails issue by learning transferrable parameters
from both optimization and feature perspectives. Specifically, we propose a
gradient alignment optimizer and adopt an adversarial training scheme to
facilitate knowledge transfer from the head to the tail. Such methods can also
deal with the cold-start problem of new users. Moreover, it could be directly
adaptive to various well-established sequential models. Extensive experiments
on four real-world datasets verify the superiority of our framework compared
with the state-of-the-art baselines.

Machine Learning;Artificial Intelligence;Information Retrieval