akiragy/recsys_pipeline
Build Recommender System with PyTorch + Redis + Elasticsearch + Feast + Triton + Flask. Vector Recall, DeepFM Ranking and Web Application.
This project helps e-commerce or media companies build and deploy a personalized recommendation system. It takes user interaction data (like movie ratings or product clicks) and generates relevant item suggestions. This is for product managers, data scientists, or machine learning engineers who need to understand or implement a full recommender system pipeline, from data preparation to a deployed web application.
No commits in the last 6 months.
Use this if you need a comprehensive, end-to-end example of building a recommender system with real-world complexities, from offline model training to online serving.
Not ideal if you're looking for a simple, single-script solution for basic recommendations without deploying multiple components.
Stars
63
Forks
15
Language
Python
License
—
Category
Last pushed
Sep 02, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/akiragy/recsys_pipeline"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
hoangsonww/The-MovieVerse-Database
🍿 Welcome to The MovieVerse: Your definitive gateway to cinema! Explore, discover, and immerse...
redis-developer/redis-nvidia-recsys
Three examples of recommendation system pipelines with NVIDIA Merlin and Redis
Sagar-Darji/CinematchAI
Multi-agent AI movie recommendation system - 6 LangGraph agents (profile analysis, content...
Abdelrhman941/Movie-Recommendation-System-Project
DEPI project about recommendation system powered by AI
truongng201/Vibelens
Vibelens is an AI-powered system that recommends the most relevant segment of a song based on an...