Design, implement, debug, and review industrial recommendation-system architecture or algorithms using released PDFs, curated examples, and runnable templates.
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Resolve constraints that change implementation: objective, QPS, P99, data/feature scale, freshness, consistency, runtime, deployment, availability, privacy, and cost.
Search the local PDF index and read cited pages. Keep 本书依据, repository convention, and engineering assumptions separate. Reuse runnable templates and label [MOCK] teaching substitutes.
Services require explicit contracts, timeouts, overload/degradation, observability, version consistency, rollback, and capacity. Models require objective, sampling, feature-time semantics, offline/online parity, complexity, and evaluation.
Run the changed path. Report 本书依据, 工程实现, 验证, and unresolved 待业务确认.
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Review recommendation-system code, architecture, experiment, or design for correctness, serving safety, offline-online consistency, capacity, observability, and rollback gaps.
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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.
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Diagnose recommendation quality, traffic, feature, model, retrieval, ranking, latency, timeout, overload, degradation, or logging regressions using an evidence-first decision tree.
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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.
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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.
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