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Claude Code Dedicated Development Harness - Achieving High-Quality Development Through an Autonomous Plan→Work→Review Cycle
A curated collection of 1000+ agent skills from official dev teams and the community, compatible with Claude Code, Codex, Gemini CLI, Cursor, and more.
Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Pre-Ranking, Ranking, Post Ranking, Relevance, LLM and RL. Please cite our p…
Paper List of Pre-trained Foundation Recommender Models
250+ Fine-tuning & RL Notebooks for text, vision, audio, embedding, TTS models.
MLGym A New Framework and Benchmark for Advancing AI Research Agents
Typer, build great CLIs. Easy to code. Based on Python type hints.
MuLe: Multi-Grained Graph Learning for Multi-Behavior Recommendation (CIKM 2024)
How Powerful are Graph Neural Networks?
RSTutorials: A Curated List of Must-read Papers on Recommender System.
A curated list of Generative Recommender Systems (Paper & Code)
A curated list of awesome Recommender System (Books, Conferences, Researchers, Papers, Github Repositories, Useful Sites, Youtube Videos)
Replication of the paper "Text Is All You Need: Learning Language Representations for Sequential Recommendation" on KDD'23.
[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).
PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.
[KDD'2023] "KGRec: Knowledge Graph Self-Supervised Rationalization for Recommendation"
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner in WWW'23
GraphMAE: Self-Supervised Masked Graph Autoencoders in KDD'22
[ICLR'2023] "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"
추천시스템 주요 논문 리뷰 및 구현
Must-read papers on graph neural networks (GNN)