Learning transferrable parameters for long-tailed sequential user behavior modeling

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
EditorsJiliang Tang, B. Aditya Prakash
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450379984
Publication statusPublished - 2020
EventACM International Conference on Knowledge Discovery and Data Mining 2020 - Virtual, Online, United States of America
Duration: 23 Aug 202027 Aug 2020
Conference number: 26th
https://dl.acm.org/doi/proceedings/10.1145/3394486 (Proceedings)
https://www.kdd.org/kdd2020/ (Website)


ConferenceACM International Conference on Knowledge Discovery and Data Mining 2020
Abbreviated titleKDD 2020
Country/TerritoryUnited States of America
CityVirtual, Online
Internet address


  • adversarial training
  • gradient alignment
  • long-tailed distribution
  • sequential user behavior modeling

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