metadata

title: Books Recommender
emoji: 📚
colorFrom: green
colorTo: red
sdk: static
pinned: false

Books Recommendation Project (BT5153)

Hello, and welcome to our books recommendation project for BT5153!

Project Directory

Front-end UI

To Add Frontend Here

Source Code

Codes are stored under ./Books as .ipynb files, and named according to the order they should be run.

Data

Data used for the project is stored in ./Data.

Raw data, retrieved from the Goodreads dataset here, can be found under ./raw-data.

For our submission, we have created a representative subset of our dataset to be included in the zip submission, and can be found in ./Data-sub.

To run our project in Windows:

Create a virtual environment (optional)

Run these commands:

python -m venv venv
venv\Scripts\activate
python -m pip install -r requirements.txt

Locate python notebooks

All python notebooks can be found in the subdirectory ./Books/.

Data preprocessing

Run all cells in the file 1_data_split.ipynb.

Generating recommendations

Run all cells in the following files:

2.1
2.2
2.3
2.4
2.5
2.6

Then, run the following file to generate recommendation for users: 3_book_to_user_converer.ipynb

Ensemble model

Run this file: 4_ensemble.ipynb

Project Description

In response to the overwhelming number of book choices online, which often leads to decision paralysis and wasted time, we propose the implementation of a Natural Language Processing (NLP) powered recommendation system to address this challenge.

For full project description, see the report file in submission.

Members:

Ang Kai En (A0221945E)
Meritxell Camp Garcia (A0280366B)
Sidharth Pahuja (A0218880X)
Sim Jun You (A0200198L)
Sim Yew Chong (A0189487A)