Embedding-Space Orthogonal Decomposition for Robust Social Recommendation
TL;DR
Orthogonal Decomposition for Social Recommendation (ODSR) is proposed, an embedding-space framework that orthogonally decomposes the aggregated social message into an aligned component and an orthogonal deviation, and learns a dimension-wise vector gate to regulate the deviation under ranking supervision.
Abstract
Graph-based social recommendation leverages both the interaction graph and the social graph to model user preferences, especially under sparse feedback. However, users' intricate social behaviors may introduce mismatched social ties that contaminate user representations and harm the models' robustness. The majority of existing methods mitigate this by pruning, rewiring, or assigning edge-wise weights before social aggregation. From users' historical behaviors, we observe that a social neighbor often overlaps with the target user on specific interests but differs in others. Thus, using a single weight for each social connection is insufficient, as it only scales the overall message intensity and fails to selectively suppress the misaligned components within the aggregated message. To fill this gap, we propose Orthogonal Decomposition for Social Recommendation (ODSR), an embedding-space framework that orthogonally decomposes the aggregated social message into an aligned component and an orthogonal deviation, and learns a dimension-wise vector gate to regulate the deviation under ranking supervision. Additionally, we introduce a contrastive regularizer that perturbs representations along deviation directions to enhance robustness against imperfect social signals. Extensive experiments on three datasets show that ODSR consistently outperforms strong baselines, and additional analyses verify the effectiveness of selectively gating the orthogonal deviation.