The advent of continuous word representation technologies such as Word2Vec and GLOVE has transformed how Data Scientists and Machine Learning experts work with natural language data. One reason these algorithms are so successful is that they offer an efficient information preserving methodology to highly compress native features (word frequencies) to the dimensions of the embedded vector space. This is particularly effective in the sparse data context of word count frequencies. Recently word embedding algorithms have been generalized to generic graph networks contexts. In this talk we review results of applying this generalization to alternative sparse data contexts such as User-based as well as Item-based recommender algorithms.