Book Crossing Recommender System

This project is developed in Python + Scikit-learn + Scikit-surprise for building a recommender system for book crossing dataset.

In this project, these recommender systems are implemented:

• User-based collaborative filtering
• Item-based collaborative filtering -> Weighted Slope One
• Content-based filtering
• Machine Learning based filtering -> Baseline

Environment

• Python: 3.9.0
• Numpy: 1.22.1
• Pandas: 1.3.5
• Matplotlib: 3.5.1
• Plotly: 5.5.0
• Scikit-learn: 1.0.2
• Scikit-surprise: 1.1.1

Setup Guide

Clone the repository:

git clone https://github.com/mohsenMahmoodzadeh/book-crossing-recommender-system

Create a virtual environment (to avoid conflicts):

virtualenv -p python3.9 recsys

# this may vary depending on your shell
. recsys/bin/activate

Install the dependencies:

pip install -r requirements.txt

If you want to work on jupyter notebook, you may need to setup a kernel on your virtual environment to make sure all your modules execute correctly.

python -m ipykernel install --name recsyskernel

# Now you get a kernel named `recsyskernel` in your jupyter notebook

Usage Guide

First, run the cells of Data_Preprocessing_and_Analysis.ipynb to generate preprocessed data files. Then you can use Recommendation.ipynb to make recommendations and test recommenders.

Future Works

• Improve content-based recommender system so that it takes multiple fields(not just bookTitle) for TF-IDF representation.

• Build a hybrid recommender system which combines collaborative and content-based filtering recommender systems.

• Build a neural recommender system(something like this).

• Modify the models so that be evaluable with evaluation metrics(maybe this can help).

Contributing

Fixes and improvements are more than welcome, so raise an issue or send a PR!