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Causal inference in recommender systems: A survey and future directions
Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …
recommender systems extract user preferences based on the correlation in data, such as …
Removing hidden confounding in recommendation: a unified multi-task learning approach
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 …
bias, which poses a great challenge for unbiased learning. Previous studies proposed …
Graph-less collaborative filtering
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 …
structured user-item interaction data for collaborative filtering (CF) task. However, with their …