Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling

10/22/2020
by   Jianwen Yin, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 8

12/29/2020

Hybrid Interest Modeling for Long-tailed Users

User behavior modeling is a key technique for recommender systems. Howev...
07/24/2020

Long-tail Session-based Recommendation

Session-based recommendation focuses on the prediction of user actions b...
05/02/2019

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the pr...
05/28/2021

Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention

Sequential recommendation plays an increasingly important role in many e...
05/22/2019

Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

Click-through rate (CTR) prediction is critical for industrial applicati...
04/06/2021

Adversarial Robustness under Long-Tailed Distribution

Adversarial robustness has attracted extensive studies recently by revea...
06/01/2021

Dual Graph enhanced Embedding Neural Network for CTR Prediction

CTR prediction, which aims to estimate the probability that a user will ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.