Answer recommendation-system questions from the released book PDFs with chapter and page citations. Use for explanations, comparisons, formulas, architecture concepts, and questions asking what the book says.
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
python3 tools/recsys_agent.py doctor..recsys-agent/index.jsonl is absent or PDFs changed, run python3 tools/recsys_agent.py index.扩展说明..recsys-agent/ is a disposable local cache. Never commit, publish, or quote it as a replacement edition of the book.
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Review recommendation-system code, architecture, experiment, or design for correctness, serving safety, offline-online consistency, capacity, observability, and rollback gaps.
The English summary is being prepared. Showing the original description.
Diagnose recommendation quality, traffic, feature, model, retrieval, ranking, latency, timeout, overload, degradation, or logging regressions using an evidence-first decision tree.
The English summary is being prepared. Showing the original description.
Design, implement, debug, and review industrial recommendation-system architecture or algorithms using released PDFs, curated examples, and runnable templates.
The English summary is being prepared. Showing the original description.
Design, implement, debug, and review industrial recommendation-system architecture or algorithms using the released book PDFs, curated examples, and runnable templates.
The English summary is being prepared. Showing the original description.
Run and extend the repository's recommendation-system projects covering the ranking funnel, two-tower ANN, point-in-time feature consistency, and online serving reliability.
The English summary is being prepared. Showing the original description.
Build a grounded recommendation-system study plan, explain a topic for a graduate student or new engineer, and track local progress. Use for learning, revision, interview preparation, and deciding what to study next.
The English summary is being prepared. Showing the original description.