Design, implement, debug, and review industrial recommendation-system architecture or algorithms using the released book PDFs, curated examples, and runnable templates.
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Resolve the constraints that change implementation: product objective, QPS, P99 latency, corpus/feature scale, update freshness, consistency, runtime, deployment stack, availability, privacy, and cost. Ask only for materially different tradeoffs.
本书依据, repository convention, and new engineering assumptions separate.[MOCK] dependencies clearly labelled and replace them only when real integration details exist.Run the changed surface. 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 released 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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