QUADEN: Discovering Latent Neighbors for Sparse Users and Items across Interaction Quadrants in Recommender System

Many recommender systems leverage Graph Neural Networks to capture user–item relations for delivering recommendations. However, the representation of nodes heavily relies on neighbors, causing limited neighbor nodes (sparse nodes) to lack expressive representation. Existing works discovered latent neighbors for sparse nodes but ignored node sparsity, resulting in the neighbor misallocation problem due to overlooking quality and quantity aspects. We propose discovering high-quality latent neighbors by progressively transferring knowledge from dense nodes by categorizing user–item interactions into four quadrants (dense user–dense item, dense user–sparse item, sparse user–dense item, and sparse user–sparse item). We leverage the node sparsity to determine the optimal quantity of latent neighbors. We propose a Domain Adaptation Network for transferring knowledge from dense to sparse quadrants without encountering the domain misalignment problem arising from the distinct representations between dense and sparse quadrants. An Enrichment Network is proposed to address the inexpressive representation problem due to limited observed interactions by enriching the sparse node representation. A Heterogeneous Graph Neural Network architecture is proposed to capture multiple relations between dense/sparse users and items. Experimental results on three benchmark datasets demonstrate the superiority of the proposed method over Graph Neural Network baselines, both with and without latent neighbors.