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Product Recommendations Engine

An open-source real-time product recommendation engine built as a Harper component. Combines co-occurrence learning, HNSW vector search, UCB exploration, and category diversity re-ranking on Harper's replicated tables. No vector database, training pipeline, or external ML infrastructure required.
TypeScript
Repo
TypeScript

Product Recommendations Engine

at Harper
April 26, 2026
at Harper
April 26, 2026
at Harper
April 26, 2026
April 26, 2026
An open-source real-time product recommendation engine built as a Harper component. Combines co-occurrence learning, HNSW vector search, UCB exploration, and category diversity re-ranking on Harper's replicated tables. No vector database, training pipeline, or external ML infrastructure required.
An open-source real-time product recommendation engine built as a Harper component. Combines co-occurrence learning, HNSW vector search, UCB exploration, and category diversity re-ranking on Harper's replicated tables. No vector database, training pipeline, or external ML infrastructure required.

Download

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An open-source real-time product recommendation engine built as a Harper component. Combines co-occurrence learning, HNSW vector search, UCB exploration, and category diversity re-ranking on Harper's replicated tables. No vector database, training pipeline, or external ML infrastructure required.

Download

White arrow pointing right
An open-source real-time product recommendation engine built as a Harper component. Combines co-occurrence learning, HNSW vector search, UCB exploration, and category diversity re-ranking on Harper's replicated tables. No vector database, training pipeline, or external ML infrastructure required.

Download

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Explore Recent Resources

Media Coverage
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Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.
Media Coverage
InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.
Renato Losio, Staff Editor at InfoQ
Renato Losio
InfoQ Staff Editor
Media Coverage

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.
Renato Losio
Aug 2026
Media Coverage

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.
Renato Losio
Media Coverage

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.
Renato Losio