abis330/DSSRec
Disentangled Self-Supervision in Sequential Recommenders
This project helps e-commerce managers and content curators improve their recommendation systems. It takes sequences of user interactions (like browsing history or purchase order) and learns to recommend the next most relevant item. The outcome is more personalized and accurate suggestions for individual users.
No commits in the last 6 months.
Use this if you manage an online store, streaming service, or content platform and want to offer more precise, personalized recommendations to your users.
Not ideal if you are looking for a general-purpose recommendation system that doesn't focus on the sequence or order of user actions.
Stars
31
Forks
8
Language
Python
License
—
Category
Last pushed
Aug 09, 2021
Commits (30d)
0
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