Dual-target disjointed cross-domain recommendation mediated via latent user preferences

Author

Dinh Hieu DO, Singapore Management UniversityFollow
Hady Wirawan LAUW, Singapore Management UniversityFollow

Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2025

Abstract

Users often navigate multiple platforms online, each characterized by its own set of scarce data. Recommender systems face a significant challenge in such fragmented environments. This paper proposes a novel approach to enhance recommendation systems by leveraging connections across distinct yet conceptually similar datasets from multiple platforms. We introduce a unique scenario of dual-target overlapping-free cross-platform recommendation, presenting a bridging mechanism to mutually improve across platforms and learn latent user preferences. Our approach addresses the data sparsity prevalent in each platform and enhances recommendation quality by harnessing redundant, rich, and similar domain data. Experiments validate the effectiveness of our method, demonstrating substantial improvements in recommendation quality.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

ACM SIGKDD Explorations Newsletter

Volume

27

Issue

1

First Page

52

Last Page

61

ISSN

1931-0145

Identifier

10.1145/3748239.3748245

Publisher

ACM

Citation

DO, Dinh Hieu and LAUW, Hady Wirawan. Dual-target disjointed cross-domain recommendation mediated via latent user preferences. (2025). ACM SIGKDD Explorations Newsletter. 27, (1), 52-61.
Available at: https://ink.library.smu.edu.sg/sis_research/10381

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://doi.org/10.1145/3748239.3748245