Stars
Short Video Segment-level User Interests Modeling in Personalized Recommendation
A unified, comprehensive and efficient recommendation library
[WWW'2023] "MMSSL: Multi-Modal Self-Supervised Learning for Recommendation"
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Github pages backend for https://differentialprivacy.org
Build resilient language agents as graphs.
🦜🔗 The platform for reliable agents.
Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
A Curated Collection of LLM resources (work in progress).
Paper List for Recommend-system PreTrained Models
A Toolbox for MultiModal Recommendation. Integrating 10+ Models...
links to conference publications in graph-based deep learning
Benchmark of federated learning. Dedicated to the community. 🤗
This project aims to collect the latest "call for reviewers" links from various top CS/ML/AI conferences/journals
Survey: A collection of AWESOME papers and resources on the large language model (LLM) related recommender system topics.
在常规推荐系统算法和系统双优化的范式下，一线公司针对单个任务或单个业务的效果挖掘几乎达到极限。从2019年我们开始关注多种信息的萃取融合，提出了OneRec算法，希望通过平台或外部各种各样的信息来进行知识集成，打破数据孤岛，极大扩充推荐的“Extra World Knowledge”。 已实践的算法包括行为数据，内容描述，社交信息，知识图谱等。在OneRec，每种信息和整体算法的集成是可插拔…
An Open Framework for Federated Learning.
Paper notes and code for differentially private machine learning
✨✨A curated list of latest advances on Large Foundation Models with Federated Learning
All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
[ACM MM 2024] Resisting Over-Smoothing in Graph Neural Networks via Dual-Dimensional Decoupling
Materials about Privacy-Preserving Machine Learning
A curated list of trustworthy deep learning papers. Daily updating...