Gaussian Copula Embeddings

Learning latent vector representations via embedding models has been shown promising in machine learning. However, most of the embedding models are still limited to a single type of observed data. We propose a Gaussian copula embedding model to learn latent vectorial representations of items in a heterogeneous-data setting. The proposed model can effectively incorporate different types of observed data and, at the same time, yield robust embeddings. We demonstrate that the proposed model can effectively learn in many different scenarios, outperforming competing models in modeling quality and task performance.