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A Novel Generative Model with Causality Constraint for Mitigating Biases in Recommender Systems
Jianfeng Deng, Qingfeng Chen, Debo Cheng +3
Accurately predicting counterfactual user feedback is essential for building effective recommender systems. However, latent confounding bias can obscure the true causal relationshi…
Mitigating Dual Latent Confounding Biases in Recommender Systems
Jianfeng Deng, Qingfeng Chen, Debo Cheng +3
Recommender systems are extensively utilised across various areas to predict user preferences for personalised experiences and enhanced user engagement and satisfaction. Traditiona…
Multi-Cause Deconfounding for Recommender Systems with Latent Confounders
Zhirong Huang, Shichao Zhang, Debo Cheng +3
In recommender systems, various latent confounding factors (e.g., user social environment and item public attractiveness) can affect user behavior, item exposure, and feedback in d…
Mitigating Propensity Bias of Large Language Models for Recommender Systems
Guixian Zhang, Guan Yuan, Debo Cheng +3
The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analy…