An open source movie recommendation WebApp build by movie buffs and mathematicians that uses cosine similarity on the backend.

Overview

Logo

Movie Pundit

Find your next flick by asking the (almost) all-knowing Movie Pundit
Jump to Project Source »

View Demo · Report Bug · Request Feature

Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Contact
  6. Acknowledgments

About The Project

Movie Pundit Action

There are many great streaming services to watch movies online in todays day and age. However, their build in content suggestion system is quite a bit broken and often times distracting, as convenient as it may be. This was the inspiration behind this Project. To iteratively build the best Movie Recommendation System that asks you what type of movie you would like to watch, no tell you what you should be watching in an intrusive way.

Why use Movie Pundit:

  • Fast and Seamless with a catalogue of 5000+ movies to boot
  • Integration with TMDB API allows you quicky read up the entire summary from IMDB itself
  • Created by movie buffs. We have painstakingly created the Content Recommendation Model from Scratch Know More »

Of course, building a recommendation system is a continuous process and requires iterative improvements and matures over time. We will be updating the model on the backend per the issues/user feedback and we aim to make the most authentic recommender on the internet!

Movie Pundit Home

Visit Movie Pundit to check it out now!

(back to top)

Built With

This project is made with :

(back to top)

Getting Started

Before you start working on this project/fork it, it is highly recommended that you check out how the model was developed here : Model ipynb

We can clone the entire project To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

  • pip
    python -m pip install –upgrade pip

Installation

Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.

  1. Get a free API Key at developers.themoviedb.org/3/getting-started/authentication
  2. Clone the repo
    git clone https://github.com/KaProDes/Movie_Pundit.git
  3. Install pip packages (It is recommended to this in a venv)
    pip install requirements.txt
  4. Edit this line by entering your API key in app.py
    my_api_key = "ENTER YOUR API_KEY"
  5. Launch the Project by writing
    streamlit run app.py

(back to top)

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

(back to top)

License

Distributed under the MIT License. See LICENSE.txt for more information.

(back to top)

Contact

Your Name - @KapProDes - [email protected]

Project Link: https://github.com/KaProDes/Movie_Pundit

(back to top)

Acknowledgments

Special thanks to all my teachers and mentors. I have made this project as part of my Social Network Analysis and Big Data Analytics practical learning.

(back to top)

Owner
Kapil Pramod Deshmukh
Web Developer. Learning the nooks and crannies of theoretical Computer Science.
Kapil Pramod Deshmukh
ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms

ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embeddi

LI, Wai Yin 90 Oct 08, 2022
This is our Tensorflow implementation for "Graph-based Embedding Smoothing for Sequential Recommendation" (GES) (TKDE, 2021).

Graph-based Embedding Smoothing (GES) This is our Tensorflow implementation for the paper: Tianyu Zhu, Leilei Sun, and Guoqing Chen. "Graph-based Embe

Tianyu Zhu 15 Nov 29, 2022
A movie recommender which recommends the movies belonging to the genre that user has liked the most.

Content-Based-Movie-Recommender-System This model relies on the similarity of the items being recommended. (I have used Pandas and Numpy. However othe

Srinivasan K 0 Mar 31, 2022
Movies/TV Recommender

recommender Movies/TV Recommender. Recommends Movies, TV Shows, Actors, Directors, Writers. Setup Create file API_KEY and paste your TMDB API key in i

Aviem Zur 3 Apr 22, 2022
This is our implementation of GHCF: Graph Heterogeneous Collaborative Filtering (AAAI 2021)

GHCF This is our implementation of the paper: Chong Chen, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu and Shaoping Ma. 2

Chong Chen 53 Dec 05, 2022
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

RecSim NG, a probabilistic platform for multi-agent recommender systems simulation. RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a power

Google Research 110 Dec 16, 2022
QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and newly state-of-the-art recommendation models are implemented.

Yu 1.4k Dec 27, 2022
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

DANSER-WWW-19 This repository holds the codes for Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recom

Qitian Wu 78 Dec 10, 2022
An Efficient and Effective Framework for Session-based Social Recommendation

SEFrame This repository contains the code for the paper "An Efficient and Effective Framework for Session-based Social Recommendation". Requirements P

Tianwen CHEN 23 Oct 26, 2022
Recommender System Papers

Included Conferences: SIGIR 2020, SIGKDD 2020, RecSys 2020, CIKM 2020, AAAI 2021, WSDM 2021, WWW 2021

RUCAIBox 704 Jan 06, 2023
An open source movie recommendation WebApp build by movie buffs and mathematicians that uses cosine similarity on the backend.

Movie Pundit Find your next flick by asking the (almost) all-knowing Movie Pundit Jump to Project Source » View Demo · Report Bug · Request Feature Ta

Kapil Pramod Deshmukh 8 May 28, 2022
Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.

COTREC Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'. Requirements: Python 3.7, Pytorch 1.6.0 Best Hype

Xin Xia 43 Jan 04, 2023
reXmeX is recommender system evaluation metric library.

A general purpose recommender metrics library for fair evaluation.

AstraZeneca 258 Dec 22, 2022
Attentive Social Recommendation: Towards User And Item Diversities

ASR This is a Tensorflow implementation of the paper: Attentive Social Recommendation: Towards User And Item Diversities Preprint, https://arxiv.org/a

Dongsheng Luo 1 Nov 14, 2021
A Python scikit for building and analyzing recommender systems

Overview Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. Surprise was designed with th

Nicolas Hug 5.7k Jan 01, 2023
The implementation of the submitted paper "Deep Multi-Behaviors Graph Network for Voucher Redemption Rate Prediction" in SIGKDD 2021 Applied Data Science Track.

DMBGN: Deep Multi-Behaviors Graph Networks for Voucher Redemption Rate Prediction The implementation of the accepted paper "Deep Multi-Behaviors Graph

10 Jul 12, 2022
NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs.

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in

420 Jan 04, 2023
A library of Recommender Systems

A library of Recommender Systems This repository provides a summary of our research on Recommender Systems. It includes our code base on different rec

MilaGraph 980 Jan 05, 2023
Cloud-based recommendation system

This project is based on cloud services to create data lake, ETL process, train and deploy learning model to implement a recommendation system.

Yi Ding 1 Feb 02, 2022
Movie Recommender System

Movie-Recommender-System Movie-Recommender-System is a web application using which a user can select his/her watched movie from list and system will r

1 Jul 14, 2022