HKUDS/SSLRec
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
This helps e-commerce managers, content platforms, or app developers improve product, movie, or service recommendations for their users. It takes user interaction data (like purchases or views) and outputs a refined recommendation model that suggests items users are more likely to engage with. The end-user is a data scientist or machine learning engineer tasked with building or enhancing recommendation systems.
558 stars. No commits in the last 6 months.
Use this if you are developing new recommendation models or need to evaluate existing ones across various scenarios like sequential recommendations or those enhanced by social networks or knowledge graphs.
Not ideal if you are a business user looking for a no-code solution to deploy a recommendation system directly.
Stars
558
Forks
77
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 21, 2025
Commits (30d)
0
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