Counterfactual learning for multimodal user preference modeling in recommendation

This work proposes a model called Counterfactual Enhanced Recommendation (CFER), aiming to improve the quality of modeling user preferences while utilizing negative item information, and captures fine-grained differences between positive and negative item using counterfactual inference combined with a disentanglement technique.