kyegomez/Tiktokx

Tiktok is an advanced multimedia recommender system that fuses the generative modality-aware collaborative self-augmentation and contrastive cross-modality dependency encoding to achieve superior performance compared to existing state-of-the-art multi-model recommenders.

27
/ 100
Experimental

This project helps e-commerce managers, content strategists, and marketing professionals enhance their recommendation systems. It takes in user interaction data combined with visual, audio, and text information about items, and outputs highly personalized, engaging recommendations. The goal is to improve user experience and drive engagement by suggesting relevant products or content.

No commits in the last 6 months.

Use this if you need to build or significantly improve a recommendation engine for platforms like e-commerce sites or media content services that deal with diverse content formats (images, videos, text).

Not ideal if you only have simple transactional data or if your main focus is on a single data type like just text, as its strength lies in combining multiple modalities.

e-commerce-recommendations content-personalization multimedia-marketing user-engagement digital-strategy
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 6 / 25

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Stars

14

Forks

1

Language

Python

License

MIT

Last pushed

Aug 18, 2023

Commits (30d)

0

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