Causal reasoning meets visual representation learning: A prospective study

Y Liu, YS Wei, H Yan, GB Li, L Lin - Machine Intelligence Research, 2022 - Springer
Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

Bias and debias in recommender system: A survey and future directions

J Chen, H Dong, X Wang, F Feng, M Wang… - ACM Transactions on …, 2023 - dl.acm.org
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

A survey on causal inference for recommendation

H Luo, F Zhuang, R Xie, H Zhu, D Wang, Z An, Y Xu - The Innovation, 2024 - cell.com
Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

Information theoretic learning-enhanced dual-generative adversarial networks with causal representation for robust OOD generalization

X Zhou, X Zheng, T Shu, W Liang… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
Recently, machine/deep learning techniques are achieving remarkable success in a variety
of intelligent control and management systems, promising to change the future of artificial …

Trustworthy recommender systems

S Wang, X Zhang, Y Wang, F Ricci - ACM Transactions on Intelligent …, 2024 - dl.acm.org
Recommender systems (RSs) aim at helping users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …

[HTML][HTML] A survey on fairness-aware recommender systems

D Jin, L Wang, H Zhang, Y Zheng, W Ding, F Xia… - Information …, 2023 - Elsevier
As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

Removing hidden confounding in recommendation: a unified multi-task learning approach

H Li, K Wu, C Zheng, Y Xiao, H Wang… - Advances in …, 2024 - proceedings.neurips.cc
In recommender systems, the collected data used for training is always subject to selection
bias, which poses a great challenge for unbiased learning. Previous studies proposed …

On the opportunity of causal learning in recommendation systems: Foundation, estimation, prediction and challenges

P Wu, H Li, Y Deng, W Hu, Q Dai, Z Dong, J Sun… - arXiv preprint arXiv …, 2022 - arxiv.org
Recently, recommender system (RS) based on causal inference has gained much attention
in the industrial community, as well as the states of the art performance in many prediction …

[PDF][PDF] StableDR: Stabilized doubly robust learning for recommendation on data missing not at random

H Li, C Zheng, P Wu - The Eleventh International Conference on …, 2023 - researchgate.net
In recommender systems, users always choose the favorite items to rate, which leads to data
missing not at random and poses a great challenge for unbiased evaluation and learning of …

Invariant preference learning for general debiasing in recommendation

Z Wang, Y He, J Liu, W Zou, PS Yu, P Cui - Proceedings of the 28th ACM …, 2022 - dl.acm.org
Current recommender systems have achieved great successes in online services, such as E-
commerce and social media. However, they still suffer from the performance degradation in …