Book Embedding Space
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A Book Embedding Space is a document embedding space composed of book embedding vectors (text-item vectors).
- Context:
- It can (typically) facilitate Semantic Analysis by placing books with similar content closer together in the space.
- It can (often) enable Clustering of books into genres or topics based on proximity within the space.
- It can range from being a simple Latent Semantic Indexing to complex Neural Network Embeddings.
- It can serve as a basis for Analogical Reasoning, where relationships between books can be explored through vector arithmetic.
- It can enable the exploration of hypothetical or intermediate content by interpolating between book vectors, revealing hidden or non-obvious connections between different subjects.
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- Example(s):
- Counter-Example(s):
- Unprocessed textual datas, which have not been transformed into vector representations and cannot be analyzed within a vector space.
- Literal Interpretations of text, where the focus is on direct meaning rather than thematic or contextual relationships expressed through embeddings.
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- See: Text Embedding, Semantic Space, Dimensionality Reduction.