Equipping Recommender Systems with Individual Fairness via Second-order Proximity Embedding

Abstract

Graph neural networks (GNNs) have been widely used for recommender systems over knowledge graphs. An important issue of GNN-based recommender systems is individual user fairness in recommendations (i.e., similar users should be treated similarly by the systems). In this paper, we make the following contributions to enable recommender systems to be equipped with individual user fairness. First, we define new similarity metrics for individual fairness, where these metrics take knowledge graphs into consideration by incorporating both first-order proximity in direct user-item interactions and second-order proximity in knowledge graphs. Second, we design a novel graph neural network (GNN) named SKIPHop for fair recommendations over knowledge graphs. By passing latent representations from both first-order and second-order neighbors at every message passing step, SKIPHop learns user embed dings that capture their latent interests present in the second-order networks. Furthermore, to realize individual user fairness, we add fairness as a regularization to the loss function of recommendation models. Finally, through experiments on two real-world datasets, we demonstrate the effectiveness of SKIPHop in terms of fairness and recommendation accuracy.