Best Practices on Recommendation Systems
- machine-learning
- recommender
- ranking
- deep-learning
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Best Practices on Recommendation Systems
State-of-the-Art Embeddings, Retrieval, and Reranking
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.8, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM5.3, Gemma4, Llava, Phi4, ...) (AAAI 2025).
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
Translated:star:Github Ranking:star: Github stars and forks ranking list. Github Top100 stars list of different languages. Automatically update daily. | Github repository ranking, automatically updated daily
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
A Python implementation of LightFM, a hybrid recommendation algorithm.
Long-term memory runtime for AI agents — plain Markdown as the source of truth, local ranked retrieval, and an independent sleep-time Manage layer. Claude Code and Codex share one store. No API key.
Module for automatic summarization of text documents and HTML pages.
allRank is a framework for training learning-to-rank neural models based on PyTorch.
A machine learning tool that ranks strings based on their relevance for malware analysis.
Open-source DeepWiki alternative — generate comprehensive wiki documentation for any codebase from terminal or browser
Studio-grade sound effects for agents: search Pixabay, Freesound, Mixkit and Kenney, ranked by human feedback
Exploit black-box recommenders for algorithm selection.