itembed

Name	itembed JSON
Version	0.5.1 JSON
	download
home_page
Summary	word2vec for itemsets
upload_time	2024-02-28 10:24:45
maintainer
docs_url	None
author
requires_python	>=3.7
license	MIT License
keywords	itemset word2vec embedding
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # `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.

            

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