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arXiv · 1709.03856

StarSpace: Embed All The Things!

Ledell Wu·Adam Fisch·Sumit Chopra·Keith Adams·Antoine Bordes·Jason Weston
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Abstract

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.

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Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston. 2017-09-12. StarSpace: Embed All The Things!. https://arxiv.org/abs/1709.03856

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