matrix-factorization
Library for matrix factorization for recommender systems using collaborative filtering (by Quang-Vinh)
fastapi
FastAPI framework, high performance, easy to learn, fast to code, ready for production (by fastapi)
| matrix-factorization | fastapi | |
|---|---|---|
| 1 | 549 | |
| 21 | 86,522 | |
| - | 3.1% | |
| 2.9 | 9.8 | |
| over 1 year ago | 1 day ago | |
| Python | Python | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
matrix-factorization
Posts with mentions or reviews of matrix-factorization.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-26.
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Recently launched my first end-to-end ML app! A film recommender system based on matrix factorization, built for Letterboxd users.
I originally tried using RiverML, which is dedicated to online ML, but after a ton of tweaking I still wasn't satisfied. In the end, I used the matrix-factorization library, which is not at all flashy but worked much, much better. By adjusting the learning rate and epochs for feeding new ratings into the model I can adjust how "personalized" the ratings are, and after a few days of messing with it I got it where I wanted it.
fastapi
Posts with mentions or reviews of fastapi.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-06-01.
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Getting Started with FastAPI: A Beginner’s Guide Using Python 🐍
Official FastAPI Docs
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How to build image search with semantic understanding
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. We use it to build the web API for the image search.
- DecipherIt: Building a NotebookLM-Inspired AI Research Assistant powered by Bright Data
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How to Set Up CI/CD for a Python Backend Application on Fly.io Using GitHub Actions
The backend of this accountability AI application is built using FastAPI, a high-performance framework for Python. The app allows users to communicate with the AI, which helps them stay accountable by generating responses based on their input. Below is a breakdown of the key components used in the code:
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🚀 Building Recallr: How I Turned PDFs into Anki Flashcards with AI
until i realized Anki has their own API Interface, which made me realize that i can automate this task, efficiently, while also getting hand on experience using FastAPI and LLMs (but am i going to change the stack later?.. lets see)
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Building Weather & History Story Cards with Python and FastAPI
By the end of this tutorial, you’ll see how I used FastAPI and Jinja2 to turn raw JSON into a dynamic timeline of weather and history that feels informative and fun.
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Build Code-RAGent, an agent for your codebase
The only thing left to do then was to build something that could showcase the power of code ingestion within a vector database, and it immediately clicked in my mind: "Why don't I ingest my entire codebase of solved Go exercises from Exercism?" That's how I created Code-RAGent, your friendly coding assistant based on your personal codebases and grounded in web search. It is built on top of GPT-4.1, powered by OpenAI, LinkUp, LlamaIndex, Qdrant, FastAPI and Streamlit. The building of this project was aimed at providing a reproducible and adaptable agent, that people can therefore customize based on their needs, and it was composed of three phases:
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Understanding the Relation Between FastAPI and Uvicorn
FastAPI and Uvicorn are two essential building blocks when developing high-performance Python APIs.
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Building a Full-Stack AI Chatbot with FastAPI (Backend) and React (Frontend)
FastAPI Documentation: https://fastapi.tiangolo.com/
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Building a Local AI Agent with Ollama + MCP + LangChain + Docker"
FastAPI to build a tool server
What are some alternatives?
When comparing matrix-factorization and fastapi you can also consider the following projects:
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
LT-OCF - LT-OCF: Learnable-Time ODE-based Collaborative Filtering, CIKM'21
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs