Search Results for author:
Found 5 papers, 2 papers with code
Counterfactual Explanation for Fairness in Recommendation
The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes.
Neural Causal Graph Collaborative Filtering
In this work, we propose to integrate causal modeling with the learning process of GCN-based GCF models, leveraging causality-aware graph embeddings to capture complex causal relations in recommendations.
Reinforced Path Reasoning for Counterfactual Explainable Recommendation
We also deploy the explanation policy to a recommendation model to enhance the recommendation.
Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' true intent and thus deteriorate the recommendation effectiveness.
Be Causal: De-biasing Social Network Confounding in Recommendation
A common practice to address MNAR is to treat missing entries from the so-called "exposure" perspective, i. e., modeling how an item is exposed (provided) to a user.
