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real-time-recommendation-analytics

End-to-end retail analytics and recommendation platform using PySpark, Kafka, PostgreSQL, Power BI, PyTorch Two-Tower, Qdrant, FastAPI, Docker and Azure.

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GITHUB · launched Sep 28, 2026 · 109 upvotes (top 8%) · 0 views

Niche: recommendation engines · 48 launches, 4 in the last 7 days · Explore this niche →

Category and tags

  • Retail & commerce
  • Analytics & BI
  • B2B
  • AI feature
  • Hobby / open-source project

Visit website ↗ Source listing ↗

Could you build this?

Partial The individual components (FastAPI, Qdrant, Docker) can be set up via prompting, but establishing a real-time distributed stream pipeline (PySpark + Kafka + PyTorch Two-Tower training/inference) requires complex infrastructure engineering.

What it would actually take: Building a working prototype involves orchestrating Kafka for clickstream ingestion, PySpark Streaming for feature aggregation, a two-tower neural network trained in PyTorch, and Qdrant for approximate nearest neighbor vector search. The difficult aspects are distributed stream state management, low-latency online inference (<50ms), and feature store consistency between training and real-time serving.

Competitors

Other products that read as similar to this one : 489 launches clear the similarity bar, closest 8 shown.

Attention rank: #24 of 490 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 334 days after the earliest competitor.

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.