Portfolio polish: verify recommender evaluation, benchmarks, coverage, security, and quickstart

[CoreyLeath-code]

Context

A senior AI engineer recommended closing the highest-impact pending items and making each flagship project tell a distinct, evidence-backed engineering story.

Goals

• Run a reproducible recommender evaluation with documented dataset/split, fixed seed, baseline, and ranking metrics appropriate to the implementation (for example Recall@K, NDCG@K, MRR, or MAP where supported).
• Verify latency percentiles and throughput for retrieval/ranking, including workload, hardware/software environment, warm-up, and sample count.
• Explain the full path from interaction data → sequence preparation → candidate retrieval/FAISS → ranking → serving/monitoring.
• Verify tests and coverage; enforce a realistic threshold in CI.
• Confirm code, dependency, secret, and container security jobs produce visible results.
• Measure Docker image size and document the exact image/tag and command.
• Validate the quickstart from a clean clone and correct missing prerequisites or hidden state.
• Surface the strongest verified metrics near the top of the README.
• Add Engineering Decisions & Trade-offs covering candidate recall versus ranking cost, cold-start behavior, stale embeddings/indexes, failure modes, and the next production step.
• Fix typos and filename inconsistencies.

Acceptance criteria

1. Evaluation and benchmark commands are reproducible.
2. Baseline comparisons and caveats are explicit.
3. CI reports tests and coverage and enforces the documented threshold.
4. Clean-clone quickstart succeeds.
5. Security and Docker evidence is visible.
6. Measured values, targets, simulations, and planned features are clearly distinguished.

Submit the work through a focused pull request with validation results. Do not invent metrics or production claims.