StarSpace

A multi-purpose learning model

StarSpace learns to represent objects of different types into a common vectorial embedding space in order to compare them against each other. This makes it well suited for a variety of problems, including:

• Learning word, sentence or document level embeddings.

• Information retrieval - ranking of sets of entities / documents or objects, e.g., ranking web documents.

• Text classification, or any other labeling task.

• Metric / similarity learning, e.g., learning sentence or document similarity.

• Content-based or Collaborative Filtering-based Recommendation, e.g., recommending music or videos.

• Embedding graphs, e.g., multi-relational graphs such as Freebase.

Additional details on StarSpace can be found in the research paper.