metadata

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

To see the files, please go to the Files tab!

Link to files: https://huggingface.co/spaces/bt5153-books/README/tree/main

Books Recommendation Project (BT5153)

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

Project Directory

Front-end UI

Book Recommendation Ensemble Model Interface

This interface generates recommendations, but only for a list of randomly sampled test users from our dataset.

This interface was created on Python version 3.11.4, with requirements listed in requirements.txt. There may be some requirements missed, please install as needed.

All sub-models and the final ensemble classifier model were trained in advance. They are included inside the Data folder.

All data used for live recommendation is in the Data folder. Since the Data folder is too large to be submitted, we will submit a representative subset of the data.

Start the interface with python -m flask run.

If for some reason app does not start, try running python app.py.

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_users_similarity.ipynb
2.2_reviews_LDA.ipynb
2.3_description_s2v.ipynb
2.4_genres_w2v.ipynb
2.5_titles_bge_faiss.ipynb
2.6_book_clustering.ipynb

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

Ensemble model

Run this file: 4_ensemble_final.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)