Developer builds local movie recommender with Corrective-RAG

By PulseAugur Editorial · [1 sources] · 2026-05-25 22:50

A developer has created a local-first movie recommendation system using Ollama and a Corrective-RAG pipeline. This system aims to provide personalized recommendations by learning from a user's entire viewing history across different platforms. Key features include hybrid retrieval, a grader-based correction loop for cited explanations, and query expansion at ingest time to improve scalability. AI

IMPACT Demonstrates a novel application of RAG for personalized recommendations, potentially inspiring similar local-first AI tools.

RANK_REASON This is a personal project showcasing a specific implementation of RAG techniques, not a commercial product release or significant research.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

dev.to — LLM tag TIER_1 English(EN) · A Aesthetic · 2026-05-25 22:50

I built a local-first movie recommender with Corrective-RAG (cited explanations, hybrid retrieval, runs entirely on Ollama)

Hey — sharing a project I've been building for the last few months. It's a movie recommendation system that runs entirely on your laptop using Ollama, with a Corrective-RAG pipeline. Why I built it: existing streaming platforms only know what you watch…

COVERAGE [1]

I built a local-first movie recommender with Corrective-RAG (cited explanations, hybrid retrieval, runs entirely on Ollama)

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