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StarSpace: General neural model for efficient learning of entity embeddings
- denimboy 4y agoStarSpace 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.