Peer-to-peer energy trading among electric vehicles (EVs) has been increasingly studied as a promising solution for improving supply-side resilience under growing charging demand and constrained charging infrastructure. While prior studies on EV-EV energy trading and related EV research have largely focused on transaction management or isolated mobility prediction tasks, the problem of identifying which charging nodes are more suitable for EV-EV trading in journey contexts remains open. We address this gap by formulating next charging nodes recommendation as a learning-to-rank problem, where each EV decision event is associated with a set of candidate charging locations. We propose a supervised ranking framework applied to a large-scale urban EV mobility dataset comprising millions of journey records and multidimensional EV trading-related features, including EV energy level, trading role, distance to charging locations, charging speed, and temporal station popularity. To account for uncertainty arising from the mobility of both energy providers and consumers, as well as the presence of multiple viable charging nodes at a decision point, we employ probabilistic relevance refinement to generate graded labels for ranking. We evaluate gradient-boosted learning-to-rank models, including LightGBM, XGBoost, and CatBoost, on EV journey records enriched with candidate charging nodes. Experimental results show that LightGBM consistently achieves the strongest ranking performance across standard metrics, including NDCG@k, Recall@k, and MRR, with particularly strong early-ranking quality, reflected in the highest NDCG@1 (0.9795) and MRR (0.9990). These results highlight the effectiveness of uncertainty-aware learning-to-rank for charging node recommendation and support improved coordination and matching in decentralized EV-EV energy trading systems.