Model-Agnostic Causal Embedding Learning for Counterfactually Group-Fair Recommendation
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DOI: 10.1109/tkde.2024.3424906
Abstract: Group-fair recommendation aims at ensuring the equality of recommendation results across user groups categorized by sensitive attributes (e.g., gender, occupation, etc.). Existing group-fair recommendation models traditionally employ original user embeddings for both training and testing,… read more here.
Keywords: group fairness; group; fair recommendation; sensitive attributes ... See more keywords
Heterophily-Aware Fair Recommendation using Graph Convolutional Networks
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DOI: 10.48550/arxiv.2402.03365
Abstract: In recent years, graph neural networks (GNNs) have become a popular tool to improve the accuracy and performance of recommender systems. Modern recommender systems are not only designed to serve end users, but also to… read more here.
Keywords: hetrofair; popularity bias; aware fair; fair recommendation ... See more keywords