RecomBook

A book recommender system using user-based Collaborative Filtering.

• Project website: https://recom.soleyman.xyz
• Dataset link: https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset

How to run locally

• Clone the repository and cd into it.
• Create a virtual environment and activate it.
• Install the requirements using pip install -r requirements.txt.
• Run the app using python app.py.
• Open localhost:5000 in your browser.

Technology stack used

• Pandas: For data manipulation and analysis.
• Flask: Web framework for Python.
• Matplotlib: For data visualization.
• Jupyter Notebook: For data exploration and visualization.
• Numpy, JavaScript, HTML, CSS, Bootstrap, etc.

Description

The dataset contains 278858 users, 1149780 ratings and 271360 books. It might take some time to load the search bar as it is using a large dataset (it sits on my economic server). All the trained models are saved in the models folder. The data folder contains the dataset and the recommender.ipynb Jupyter notebooks used for data exploration and visualization.

• Content-based filtering is used to provide popular 50 books to users.
• The logic for user-based collaborative filtering is how many votes a user has given to a book. Books with more than 250 votes are considered for popularity.
• Number of votes and average rating of a book is shown in the popular books section.

Then, user-based collaborative filtering is used to recommend books to users based on their ratings.

• Entering at least 3 characters in the search bar will show the top 5 books that match the search query.
• It searches within the entire exported model, not just the popular 50 books.

• Once user enter a book name, and press the search button, the recommended 10 books will be shown.
• For example, if a user likes the book 1984, the system will recommend 10 books that are similar to it.

License

MIT License