ksalama/data2cooc2emb2ann
Learning embeddings from item co-occurrence statistics, and building an approx. nearest neighbour index
This project helps you understand relationships between items by analyzing how often they appear together in your data. It takes your tabular data, identifies co-occurring items, and generates numerical representations (embeddings) that capture their relatedness. These embeddings are then used to quickly find items similar to a given item, making it useful for data scientists and analysts exploring item relationships.
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Use this if you have tabular data and need to discover and quantify how items relate to each other, and then efficiently search for similar items.
Not ideal if you're looking for a simple keyword search or don't have existing co-occurrence data.
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Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Mar 24, 2023
Commits (30d)
0
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