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.

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.