contextual-bandit-offer-recommendation
Real-time offer recommendation with Bayesian Thompson Sampling. SQL feature pipelines, a FastAPI service on Docker and AWS with a sub-100 ms target, automated retraining, offline evaluation and backtesting against random, greedy and UCB, and monitoring for regret, allocation, latency and drift.
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GITHUB · launched Oct 19, 2026 · 16 upvotes (top 71%) · 0 views
Niche: machine learning developer tools · 303 launches, 16 in the last 7 days ▼ -6% · Explore this niche →
Category and tags
- Horizontal
- Model & infra
- B2B
- AI-native
- Hobby / open-source project
- aws
- bayesian-inference
- contextual-bandits
- docker
- fastapi
- mlops
- +5 more
Visit website ↗ Source listing ↗
More like this: all 303 launches in machine learning developer tools →
Could you build this?
Yes It is a standard MLOps reference build: SQL feature pipelines, a FastAPI Thompson sampling service in Docker, offline bandit evaluation, and monitoring, all of which are well-documented patterns a capable builder can assemble with AI help.
Competitors
Other products that read as similar to this one : 588 launches clear the similarity bar, closest 8 shown.
Attention rank: #431 of 589 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 355 days after the earliest competitor.
- real-time-recommendation-analytics · github · 2026-09-28 · 109 upvotes · similarity 0.57
- macau-hidden-bites · github · 2026-10-21 · 73 upvotes · similarity 0.47
- genpark-multi-armed-bandit-ucb1-thompson-sampling-skill · github · 2026-09-09 · 8 upvotes · similarity 0.46
- eikos · github · 2026-09-23 · 25 upvotes · similarity 0.46
- von · github · 2026-09-18 · 455 upvotes · similarity 0.46
- jev-benchmarks · github · 2026-09-17 · 15 upvotes · similarity 0.46
- XQL, a query language for screening market data · hn · 2026-07-30 · 5 upvotes · similarity 0.46
- Bev · github · 2026-09-23 · 13 upvotes · similarity 0.45
The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.