Skip to content

README.md

Latest commit

 

History

History
160 lines (129 loc) · 5.24 KB

README.md

File metadata and controls

160 lines (129 loc) · 5.24 KB

Recommender Systems 👨🏼‍💻

Report Bug 🐛 · Request Feature 🐣

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

About The Project

This repository holds user & item-based recommender systems in python 🧑🏽‍💻.

what is a recommender system? 🤔

Recommender systems are the systems that are designed to recommend things to the user based on many different factors Types of recommender systems: Collaborative Recommender system, Content-based recommender system, Demographic-based recommender system, Utility-based recommender system, Knowledge-based recommender system, and Hybrid recommender system & many more 🐣

Types covered in this repository

  • Item Based 📱: a form of collaborative filtering for recommender systems based on the similarity between items calculated using people's ratings of those items.
  • User-Based 🙋‍♂️: model finds the relation between user to user & check the probability of item getting selected by the particular user.
  • Hybrid 👽: model find relations between items first then relations b/w users.
    in short
    It works as an item-based recommender system first & after it switches to a user-based recommender.
    still confused? 🥲 
    if len(dataset > 100,000,000):
     userBasedRecommender() # 🤡
else:
     itemBasedRecommender() # 🤧

Dataset

we will be using MovieLens data set, which consists of 100,000 movies along with ratings.

Built With

  • Python3 🐍
  • pandas 🐼
  • numpy 🧠
  • scipy ⚛️
  • tensorflow 🤖

Getting Started

you need a computer/laptop 🐒

Structure

code/
┣ ml-100k/                          # movie dataset
┣ item_based_filtering.py           # item based recommender system
┣ predict_rating.py                 # predict rating of a movie based on user
┣ recommender_system.py             # normal recommender system
┣ user_recommend.py                 # user based recommender system

Prerequisites

Installation

  1. Clone the repo 
    git clone https://github.com/ankit5577/recommender_system.git
  2. Go to Folder 
    cd code
  3. Run Python File 
    # for user based recommender system
python user_recommend.py

# for item based recommender system
python item_based_filtering.py

# mixed recommender
python recommender_system.py

Usage

clone the repo 👉 run the recommender you want 👉 let it train & run 🧑🏽‍💻

Contributing

For AiBoost.

  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

License

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

Contact

Ankit Kaushal - @ankit55771 - ankit@aiboost.in