smallevals — CPU-fast, GPU-blazing fast offline retrieval evaluation for RAG systems with tiny QA models.
Support-ticket dedup & resolution finder — pgvector semantic retrieval evaluated with Netflix XP-style offline replay (recall@k / MRR). Shares a domain-agnostic similarity engine with its sibling repo 'clause'. Next.js 15 · Supabase · HF embeddings.
Temporal offline evaluation for recommendation policies, with explicit causal boundaries.
Offline prototype for intent-aware queue adaptation in music recommendation systems
CTR/ranking fundamentals practice with feature crossing, Logistic Regression baselines, AUC/LogLoss/nDCG notes and reproducible evaluation scripts.
Algorithm Intern Candidate | Recommendation / Search Retrieval / Ranking | PyTorch + Faiss | Offline Evaluation / Negative Sampling / Badcase Analysis
MovieLens 1M 推荐系统：无泄漏时间切分与 Top-K 评测 / Leakage-safe temporal splitting and Top-K recommender evaluation.
可复现的中文离线内容推荐应用原型，覆盖合成行为数据、动态兴趣画像、双路召回、个性化排序、多样性重排、推荐解释与离线评估。
Playground for reproducible Harbor eval tasks that test local coding agents with offline, Docker-backed verifiers.
Offline RAG retrieval-quality harness. Recall@k, nDCG, MRR, chunking diagnostics, regression diffs. No LLM-as-judge required. CI-friendly.
Neural Thompson Sampling contextual bandit for personalized Type 2 Diabetes therapy selection — training pipeline, offline policy evaluation (IPS/SNIPS/DM/DR), safety gates, drift monitoring, and LLM-generated clinical explanations.
Lightweight Python library for interactive demo and inspection of recommender systems in Streamlit.
Spotify-style music discovery platform with Spotify OAuth, hybrid recommendation, ALS, Word2Vec-style embeddings, explainable recommendations, and offline evaluation.
Controlled study of offline recommender model-selection stability across logging regimes and behavioral targets.
End-to-end joke recommendation system with offline evaluation, FastAPI serving, Docker, and model artifacts.
Budgeted high-fidelity evaluation for symbolic regression
Production-ready recommender system suite: serving API, pipelines, algorithm SDK, and evaluation tooling.
Leakage-aware multi-retriever fusion study on MovieLens-1M, with a reviewable FunRec service overlay.
Evaluation-driven MovieLens-1M recommender with temporal holdouts, NeuMF/MF/CF/content baselines, full-candidate ranking, fusion, popularity trade-offs, and controlled metric-audit experiments.
Statistical testing for LLM evaluations: power analysis, hypothesis testing, and RAG/agent evaluation case studies. Companion notebooks for the O'Reilly live course by Rudrendu Paul.
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