liuqidong07/DiffuASR

[CIKM'23] The official implementation code of DiffuASR.

37
/ 100
Emerging

This tool helps e-commerce managers and content curators improve their personalized recommendations. By taking existing customer interaction data, it generates more diverse and representative user behavior sequences. The output is an enriched dataset that can lead to better product suggestions and content discovery for individual users.

No commits in the last 6 months.

Use this if you need to enhance your existing sequential user data to train more robust and accurate recommendation models.

Not ideal if you are looking for a pre-built recommendation system rather than a method to augment your dataset for one.

e-commerce recommendations customer journey analysis personalization data augmentation user behavior modeling
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 13 / 25

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Stars

43

Forks

6

Language

Python

License

MIT

Last pushed

May 07, 2024

Commits (30d)

0

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