This repository contains machine learning models of Movie Recommender System, designed to be deployed using ONNX and utilized in a Streamlit-based web application. The app provides an interactive interface for performing this task using neural network architectures. Check here to see other ML tasks.
For more information about the training process, please check the mov-recsys.ipynb file in the training folder.
If you encounter message This app has gone to sleep due to inactivity, click Yes, get this app back up! button to wake the app back up.
If the demo page is not working, you can fork or clone this repository and run the application locally by following these steps:
-
Clone the repository:
git clone https://github.com/verneylmavt/st-mov-recsys.git cd st-mov-recsys -
Install the required dependencies:
pip install -r requirements.txt
-
Run the Streamlit app:
streamlit run app.py
Alternatively you can run jupyter notebook demo.ipynb for a minimal interface to quickly test the model (implemented w/ ipywidgets).
I acknowledge the use of the MovieLens 32M dataset provided by GroupLens Research at the University of Minnesota. This dataset has been instrumental in conducting the research and developing this project.
- Dataset Name: MovieLens 32M
- Source: https://grouplens.org/datasets/movielens/32m/
- License: The dataset may be used for research purposes under specific conditions; please refer to the usage license for details.
- Description: This dataset contains over 32 million ratings and 2 million tag applications applied to 87,585 movies by 200,948 users, collected from January 9, 1995, to October 12, 2023.
I deeply appreciate the efforts of GroupLens Research in making this dataset available.