We present StarSpace, a general-purpose neural embedding model that can solve
a wide variety of problems: labeling tasks such as text classification, ranking
tasks such as information retrieval/web search, collaborative filtering-based
or content-based recommendation, embedding of multi-relational graphs, and
learning word, sentence or document level embeddings. In each case the model
works by embedding those entities comprised of discrete features and comparing
them against each other — learning similarities dependent on the task.
Empirical results on a number of tasks show that StarSpace is highly
competitive with existing methods, whilst also being generally applicable to
new cases where those methods are not.