23/08/2020

Learning transferrable parameters for long-tailed sequential user behavior modeling

Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun, Steven C. H. Hoi

Keywords: long-tailed distribution, adversarial training, gradient alignment, sequential user behavior modeling

Abstract Paper Similar Papers

Abstract: 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.

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3394486.3403078#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at KDD 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 19:46 
 0
 0
 0
 0
 9:53 
 0
 0
 0
 0
 15:56 
 0
 0
 0
 0
 5:00 
 0
 0
 0
 1
 12:10 
 0
 0
 0
 0
 14:47 
 0
 0
 0
 0
 9:24 
 0
 0
 0
 1
 5:10 
 0
 0
 0
 0
 3:23 
 0
 0
 0
 0
 8:04 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 3:12 
 0
 0
 0
 0
 4:21 
 0
 0
 0
 0
 2:06 
 0
 0
 0
 0
 3:04 
 0
 0
 0
 0
 12:27 
 0
 0
 0
 0
 10:34 
 0
 0
 0
 0
 10:41 
 0
 0
 0
 0
 13:53 
 0
 0
 0
 0
 6:48 
 0
 0
 0
 0
 11:49 
 0
 0
 0
 0
 15:04 
 0
 0
 0
 0
 5:00 
 0
 0
 0
 0
 10:07 
 0
 0
 0
 0
 14:49 
 0
 0
 0
 0
 16:11 
 0
 0
 0
 0
 19:44 
 0
 0
 0
 0
 8:51 
 0
 0
 0
 0
 2:59 
 0
 0
 0
 0
 17:41 
 0
 0
 0
 0
 18:24 
 0
 0
 0
 0
 14:19 
 0
 0
 0
 0
 17:08 
 0
 0
 0
 0
 2:53 
 0
 0
 0
 0
 5:18 
 0
 0
 0
 0
 15:35 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 12:19 
 0
 0
 0
 0
 19:14 
 0
 0
 0
 0
 11:50