- (Wu et al., 2017) ⇒ Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, and Jason Weston. (2017). “StarSpace: Embed All The Things!.” In: arXiv preprint arXiv:1709.03856.
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.
|2017 StarSpaceEmbedAllTheThings||Ledell Wu|
|StarSpace: Embed All The Things!||2017|