Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
1-2026
Abstract
We study a matrix completion problem where both the ground truth R matrix and the unknown sampling distribution P over observed entries are low-rank matrices, and share a common subspace. We assume that a large amount M of unlabeled data drawn from the sampling distribution P is available, together with a small amount N of labeled data drawn from the same distribution and noisy estimates of the corresponding ground truth entries. This setting is inspired by recommender systems scenarios where the unlabeled data corresponds to ‘implicit feedback’ (consisting in interactions such as purchase, click, etc. ) and the labeled data corresponds to the ‘explicit feedback’, consisting of interactions where the user has given an explicit rating to the item.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence (AAAI‑26), Singapore, January 20-27
First Page
1
Last Page
21
Publisher
AAAI
City or Country
United States
Citation
LEDENT, Antoine; SOO, Mun Chong; and NONG, Minh Hieu.
Generalization bounds for semi‑supervised matrix completion with distributional side information. (2026). Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence (AAAI‑26), Singapore, January 20-27. 1-21.
Available at: https://ink.library.smu.edu.sg/sis_research/10851
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://arxiv.org/abs/2511.13049