Like2Vec is a neaborhood based recommendation algorithm that uses network embeddings to create latent representations of transactional history
The components of the pipeline are:
├── LICENSE
├── README.md <- The top-level README for developers using this project.
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├── docs <- A default Sphinx project; see sphinx-doc.org for details
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jja-initial-data-exploration`.
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├── references <- Data dictionaries, manuals, and all other explanatory materials.
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├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
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├── src <- Source code for project.
│ ├── embeddings <- Module to create network embeddings.
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│ ├── llr <- Module to calculate log-likelihood ratio of transactions
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│ ├── prediction <- Module to create neighborhood-based predictions
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│ ├── evaluation <- Module to evaluate predictions
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