%0 Generic %1 wu2017starspace %A Wu, Ledell %A Fisch, Adam %A Chopra, Sumit %A Adams, Keith %A Bordes, Antoine %A Weston, Jason %D 2017 %K deep_learning embedding %T StarSpace: Embed All The Things! %U http://arxiv.org/abs/1709.03856 %X 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.

@misc{wu2017starspace, 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.}, added-at = {2017-09-16T15:07:22.000+0200}, author = {Wu, Ledell and Fisch, Adam and Chopra, Sumit and Adams, Keith and Bordes, Antoine and Weston, Jason}, biburl = {https://www.bibsonomy.org/bibtex/267aa5262d32b09a79e591e516b55d329/crack521}, description = {StarSpace: Embed All The Things!}, interhash = {e83438f3dcdc19a3db05153e58a5c6b6}, intrahash = {67aa5262d32b09a79e591e516b55d329}, keywords = {deep_learning embedding}, note = {cite arxiv:1709.03856}, timestamp = {2017-09-16T15:07:22.000+0200}, title = {StarSpace: Embed All The Things!}, url = {http://arxiv.org/abs/1709.03856}, year = 2017 }