To overcome the problem of data sparsity in recommendation systems, cross-domain recommendation (CDR) models leverage user data from a source domain to improve recommendations in a target domain. However, different CDR models capture unique and often complementary aspects of user preferences. This paper introduces EmbMerge, a supervised method to fuse the outputs of these diverse CDR models to produce a better, unified ranking. Traditional fusion methods are mainly based on sparse features that do not capture deep semantic relationships. EmbMerge instead employs rather small dense vector representations for models, items, ranks, and scores, processed by a transformer encoder to generate rich, context-aware embeddings. We propose two architectural variants and conduct experiments on two datasets using ranked lists from three different state-of-the-art CDR systems. Our results demonstrate that EmbMerge outperforms four baseline fusion methods, validating its effectiveness as a technique for combining the strengths of various cross-domain recommendation systems.