StarSpace is a general-purpose neural model for efficient learning of entity embeddings for solving a wide variety of problems:
- 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.
- Image classification, ranking or retrieval (e.g. by using existing ResNet features).
In the general case, it learns to represent objects of different types into a common vectorial embedding space, hence the star (‘*’, wildcard) and space in the name, and in that space compares them against each other. It learns to rank a set of entities/documents or objects given a query entity/document or object, which is not necessarily the same type as the items in the set.