Causal inference in recommender systems: A survey and future directions

C Gao, Y Zheng, W Wang, F Feng, X He… - ACM Transactions on …, 2024‏ - dl.acm.org
Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …

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

H Li, K Wu, C Zheng, Y **ao, H Wang… - Advances in …, 2023‏ - 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 …

Graph-less collaborative filtering

L **a, C Huang, J Shi, Y Xu - Proceedings of the ACM Web Conference …, 2023‏ - dl.acm.org
Graph neural networks (GNNs) have shown the power in representation learning over graph-
structured user-item interaction data for collaborative filtering (CF) task. However, with their …