word2vec for itemsets
Project description
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:
- Modifying the base algorithm to handle unordered sequences, which has an impact on the definition of context windows;
- 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:
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 for detailed explanations and examples.
Project details
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itembed-0.5.1.tar.gz
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