westlake-repl/IDvs.MoRec
End-to-end Training for Multimodal Recommendation Systems
This project helps e-commerce companies, content platforms, and online service providers improve their recommendation engines. It takes in product details (text descriptions, images) and user interaction data to generate more personalized and effective recommendations for users. E-commerce managers, content strategists, or product owners who want to enhance user engagement and conversion rates through better recommendations would use this.
166 stars. No commits in the last 6 months.
Use this if you manage a platform with a wide array of products or content and want to leverage both textual descriptions and visual information to offer highly relevant suggestions to your users.
Not ideal if your recommendation needs are simple, you only deal with ID-based recommendations without rich media, or you lack the computational resources for complex multimodal model training.
Stars
166
Forks
22
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 02, 2025
Commits (30d)
0
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