I wrote a beginner-friendly book on recommender systems — the full runnable code companion is free on GitHub
After months of work, I finished "Recommender Systems: From Ratings to Retrieval" — 13 chapters going from ratings basics through two-tower retrieval, SASRec, GNNs, and LLM-era recommenders. Every chapter has Python code, and every number in the book was produced by actually running it on MovieLens…
Read the full story at r/learnmachinelearning ↗
Timeline · 1 report
- 2026-09-21 00:41 · r/learnmachinelearning
I wrote a beginner-friendly book on recommender systems — the full runnable code companion is free on GitHub
More stories
- Gemini Hacked Three Companies in First Known Breakout by Google’s AI — Wall Street Journal Technology
- Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
- Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
- Anthropic, OpenAI, SpaceXAI, Google sued over call to ‘pace’ AI development — Politico Technology
- NVIDIA CEO Jensen Huang rejects ‘AI will end the world’ claim, yet cautions ‘we should go as fast as we can but...’ — Mint AI
- AI hallucination of Chinese nuclear components almost led to US military attack — Ars Technica AI
- The new AgentCore runtime: Elastic, optimized, and consistently fast starts — AWS Machine Learning Blog
- Qwen Image 2.1 PR to ComfyUI — r/StableDiffusion
Get the daily brief of stories like this at 6:30 every morning →