Dynamic Scene Graph Generation (DSGG) models how object relations evolve over time in videos. However, existing methods are trained only on annotated object pairs and lack guidance for non-related pairs, making it difficult to identify meaningful relations during inference. In this paper, we propose Relation Scoring Network (RS-Net), a modular framework that scores the contextual importance of object pairs using both spatial interactions and long-range temporal context. RS-Net consists of a spatial context encoder with learnable context tokens and a temporal encoder that aggregates video-level information. The resulting relation scores are integrated into a unified triplet scoring mechanism to enhance relation prediction. RS-Net can be easily integrated into existing DSGG models without architectural changes. Experiments on the Action Genome dataset show that RS-Net consistently improves both Recall and Precision across diverse baselines, with notable gains in mean Recall, highlighting its ability to address the long-tailed distribution of relations. Despite the increased number of parameters, RS-Net maintains competitive efficiency, achieving superior performance over state-of-the-art methods.