Doubly Intention Learning for Cold-start Recommendation with Uncertainty-aware Stochastic Meta Process

Huafeng Liu*, Mingjie Zhou, Liping Jing, Michael K. Ng

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

The cold-start recommendation has been one of the most central problems in online platforms where new users or items arrive continuously. Although existing meta-learning based models with globally sharing knowledge show good performance in most cold-start scenarios, the ability to handle challenges on intention heterogeneity and prediction uncertainty is missing, and these two challenges are particularly evident in cold-start scenarios with fewer interaction data. To tackle the above challenges, in this paper, we present an uncertainty-aware Stochastic Meta Process with Doubly Intention learning (DISMP) for the cold-start recommendation, which has promising properties in uncertainty quantification. With the aid of the meta-learning stochastic process, DISMP can store general knowledge by capturing the relevance of different user-item pairs in terms of intentions and concepts, which is capable of rapid adaptation to new users and items. Furthermore, intentions with general and specific levels are extracted by doubly distinguishing the role of latent variables, which is able to capture the dependencies across different types of intentions and concepts. Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines on cold-start recommendations with different perspectives.

Original languageEnglish
Title of host publicationMM 2023 - Proceedings of the 31st ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery (ACM)
Pages6212-6222
Number of pages11
Edition1st
ISBN (Electronic)9798400701085
DOIs
Publication statusPublished - 29 Oct 2023
Externally publishedYes
Event31st ACM International Conference on Multimedia, MM 2023 - Ottawa, Canada
Duration: 29 Oct 20233 Nov 2023
https://dl.acm.org/doi/proceedings/10.1145/3581783 (Conference proceedings)
https://www.acmmm2023.org/ (Conference website)

Publication series

NameProceedings of the ACM International Conference on Multimedia

Conference

Conference31st ACM International Conference on Multimedia, MM 2023
Country/TerritoryCanada
CityOttawa
Period29/10/233/11/23
Internet address

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

User-Defined Keywords

  • cold-start recommendation
  • meta learning
  • recommendation system
  • stochastic process
  • uncertainty quantification

Fingerprint

Dive into the research topics of 'Doubly Intention Learning for Cold-start Recommendation with Uncertainty-aware Stochastic Meta Process'. Together they form a unique fingerprint.

Cite this