Recommender Systems 👨🏼‍💻

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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
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

• python - https://www.python.org/downloads/
• Machine Learning libraries tensorflow, pandas, numpy, scipy, nltk:

  pip install tensorflow, pandas, numpy, scipy, nltk

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 - [email protected]