DANG: Decoupled Adaptive Normalized Graph Convolutional Network for Sign-Aware Recommendation

Abstract

Graph Neural Networks (GNNs) are widely used in recommendation systems, but most methods focus solely on positive feedback, neglecting the avoidance preferences inherent in negative interactions. Although recent sign-aware methods have attempted to integrate bidirectional feedback, they suffer from two major limitations: 1) directly incorporating negative feedback into graph topological propagation causes the homophily assumption to fail, leading to feature contamination; 2) when dealing with long-tail distributions, the underlying aggregation mechanisms of traditional GNNs lack flexibility, making them prone to passively absorbing polarity noise during deep propagation, which further exacerbates feature contamination. To enhance model robustness in complex polarity scenarios, this paper proposes a Decoupled Adaptive Normalized Graph Convolutional Network (DANG). The framework is designed from three perspectives: structural propagation, layer-wise aggregation, and optimization objective. First, positive interactions are retained within the graph topology for collaborative propagation, while negative interactions are modeled independently using a Multilayer Perceptron (MLP). This structurally mitigates feature contamination caused by the mixing of positive and negative semantics. Second, parameterized normalization and tunable layer-wise pooling mechanisms are introduced to dynamically adjust neighbor aggregation strength and cross-layer information allocation, thereby reducing the dilution of personalized user representations caused by deep smoothing signals. Finally, a sign-aware Bayesian Personalized Ranking (sBPR) objective is constructed to more accurately capture users’ preference and avoidance intentions. Experiments on three real-world datasets—Amazon-Music, Epinions, and KuaiRec—demonstrate that DANG outperforms 12 representative baseline models, including LightGCN and GFormer, in both Recall@20 and NDCG@20. Compared to the strongest baseline, DANG achieves an average improvement of 3.43% in Recall@20 and 2.42% in NDCG@20 across the three datasets. Triplicate experiments and ablation studies further indicate that the performance improvement of DANG is stable, and that decoupled modeling, adaptive normalization, and tunable layer-wise pooling all contribute positively to alleviating feature contamination and over-smoothing issues.