Metadata-Version: 2.1
Name: itembed
Version: 0.5.1
Summary: word2vec for itemsets
Author-email: Johan Berdat <jojolebarjos@gmail.com>
License: MIT License
Project-URL: Documentation, https://sdsc-innovation.github.io/itembed/
Project-URL: Repository, https://github.com/sdsc-innovation/itembed.git
Project-URL: Issues, https://github.com/sdsc-innovation/itembed/issues
Keywords: itemset,word2vec,embedding
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Utilities
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numba <1.0,>=0.34
Requires-Dist: scipy <2.0,>=0.16
Requires-Dist: tqdm >=1.0

# `itembed` — Item embeddings

This is yet another variation of the well-known word2vec method, proposed by Mikolov et al., applied to unordered sequences, which are commonly referred as itemsets.
The contribution of `itembed` is twofold:

 1. Modifying the base algorithm to handle unordered sequences, which has an impact on the definition of context windows;
 2. Using the two embedding sets introduced in word2vec for supervised learning.

A similar philosophy is described by Wu et al. in StarSpace and by Barkan and Koenigstein in item2vec.
`itembed` uses Numba to achieve high performances.


## Getting started

Install from [PyPI](https://pypi.org/project/itembed/):

```
pip install itembed
```

Or install from source, to ensure latest version:

```
pip install git+https://github.com/sdsc-innovation/itembed.git
```

Please refer to the [documentation](http://sdsc-innovation.github.io/itembed) for detailed explanations and examples.
