claude

recommendation-systems-engineering

Recommendation-systems-engineering team — the RECSYS layer that turns interaction data into ranked recommendations. agents (recsys-architect, recsys-implementation-engineer) covering approach selection (popularity baseline, collaborative filtering, content-based, hybrid, two-tower/embedding retrieval, sequential), the candidate-generation → ranking → re-ranking pipeline, cold-start (new user/new item), offline evaluation (recall@k, nDCG, MAP, coverage/diversity) AND online A/B, feature stores with train/serve parity, low-latency serving, and feedback-loop/position bias. skills, Mermaid-backed knowledge docs, templates. Seams: training infra → ml-engineering; keyword/semantic search → search-relevance-engineering; A/B + stats → experimentation-growth-engineering + applied-statistics. House line: baseline before a neural net; offline wins must survive an online A/B. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.1.2MITupdated today
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Plugin recommendation-systems-engineering from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

What it installs

Skills (3)

  • choose-recsys-approach

    Choose the recommendation approach — popularity/heuristic baseline, collaborative filtering, content-based, hybrid, two-tower/embedding retrieval, or sequential — from data volume, sparsity, cold-start severity, latency, and interpretability. Use when starting a recsys or deciding whether to add mod

  • evaluate-recommenders

    Evaluate recommenders correctly — a temporal train/test split, per-stage offline metrics (recall@k, precision@k, nDCG, MAP, coverage, diversity, novelty) against a popularity baseline, AND the online A/B that is the real verdict. Use to build an eval harness or diagnose an offline-vs-online gap.

  • handle-cold-start-and-serving

    Handle cold-start (new users and new items) and serve recommendations within a latency budget — content/popularity fallbacks, onboarding & exploration, ANN retrieval, precompute/caching, online feature fetch, and popularity-on-timeout fallback. Use when new entities recommend poorly or serving is to

Subagents (2)

  • recsys-architect

    Use for recsys DESIGN + eval strategy — 'collaborative vs content vs hybrid vs two-tower?', 'candidate-gen → ranking pipeline?', 'cold-start plan?', 'offline won but A/B flat — why?'. Baseline before neural net; offline wins must survive an A/B. NOT keyword search → search-relevance-engineering.

  • recsys-implementation-engineer

    Use to BUILD recommenders — implement candidate generation (ANN/embedding retrieval), ranking models, re-ranking, feature pipelines, offline eval harness, and low-latency serving. Python-first. NOT the approach/eval strategy → recsys-architect; NOT training platform → ml-engineering.

View source on GitHub mcorbett51090.github.io

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