jeongwhanchoi/SCONE

"SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation", WSDM 2025

43
/ 100
Emerging

This project helps e-commerce managers, content strategists, and marketing analysts improve their recommendation systems. It takes existing user interaction data and product information to generate more effective and diverse product suggestions. The output is a refined recommendation model that provides better personalization and engagement for end-users.

Use this if you are building or optimizing a recommendation system and struggle with generating diverse and relevant suggestions, especially with sparse user interaction data.

Not ideal if you need a plug-and-play solution without any technical implementation, as this is a research-oriented framework for developers.

e-commerce recommendations content personalization product discovery user engagement data sparsity
No Package No Dependents
Maintenance 6 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 15 / 25

How are scores calculated?

Stars

15

Forks

5

Language

Python

License

MIT

Last pushed

Nov 25, 2025

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/jeongwhanchoi/SCONE"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.