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.
Original language | English |
---|---|
Title of host publication | Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining |
Editors | Jiliang Tang, B. Aditya Prakash |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 359-367 |
Number of pages | 9 |
ISBN (Electronic) | 9781450379984 |
DOIs | |
Publication status | Published - 2020 |
Event | ACM International Conference on Knowledge Discovery and Data Mining 2020 - Virtual, Online, United States of America Duration: 23 Aug 2020 → 27 Aug 2020 Conference number: 26th https://dl.acm.org/doi/proceedings/10.1145/3394486 (Proceedings) https://www.kdd.org/kdd2020/ (Website) |
Conference
Conference | ACM International Conference on Knowledge Discovery and Data Mining 2020 |
---|---|
Abbreviated title | KDD 2020 |
Country/Territory | United States of America |
City | Virtual, Online |
Period | 23/08/20 → 27/08/20 |
Internet address |
|
Keywords
- adversarial training
- gradient alignment
- long-tailed distribution
- sequential user behavior modeling