LitAura is a mood-based book recommender web app that suggests books according to your emotions. Built using HTML, CSS, JavaScript, and Botpress integration, it provides an interactive chatbot that helps users discover the right book based on their current mood.
Welcome to my Book-Recommender-System!
Semantic book recommendation system leveraging vector embeddings (sentence-transformers), approximate nearest neighbor search (FAISS), and generative AI (Google Gemini/Vertex AI) for personalized analysis and content generation, wrapped in an interactive Gradio web UI.
This is a website that I built to showcase all of my books I currently have!
This project is the second submission for the Applied Machine Learning class on Dicoding, focusing on a content-based book recommendation system using TF-IDF and Cosine Similarity based on genre similarity. This project is also part of the CET Dicoding Program course completion.
Book Recommender Chatbot is a rule-based web app built with Python and Flask. It uses popularity and collaborative filtering to recommend books based on user preferences, offering personalized suggestions via a chat interface.
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