NovelNudge is a book recommendation engine that embeds titles, descriptions, authors, and genres using SentenceTransformers. It combines these vectors and ranks similar books with cosine similarity and fuzzy title matching.

A social book review and discovery platform with real-time, personalized recommendations using FAISS and LDA. Built with Next.js, Tailwind CSS, and TypeScript.

Hybrid book recommendation system using NLP (TF-IDF, cosine similarity) and collaborative filtering (SVD) to generate personalized book suggestions with high accuracy. Includes full EDA, model evaluation.

Book recommendation system using rank-based, user-based, and SVD collaborative filtering on the Goodreads dataset.

A collaborative filtering-based book recommendation system that analyzes user ratings, computes similarities, and generates personalized book suggestions using Python and matrix techniques.

Maven Analytics Goodreads Challenge Submission

AI-powered book recommendation chatbot using IBM Watson Assistant 💬📚

A simple Book Recommendation System that suggests books to users based on similarity scores using content-based filtering. Built with Python, it helps users discover books they might enjoy by analyzing features like title, author, and genre.

Preflexion

Book recommendation system using machine learning and similarity algorithms to deliver personalized suggestions.

📚 A Book Recommendation System built with Python, Flask, and machine learning. It suggests books based on user preferences using collaborative filtering and popularity-based models.

End-to-end RAG system for intelligent book search and recommendations using LLMs, vector embeddings, and Qdrant.

AI-powered book discovery and chat application built with ASP.NET Core 8 and Google Gemini API. Features include AI chat for book recommendations, Google Books search, user authentication, and chat history.

This project implements a book recommendation system using machine learning techniques. It helps users find books based on preferences, book ratings, and similarity measures.

Build a book recommendation system using machine learning algorithms like collaborative filtering and content-based filtering. This system suggests personalized books based on user ratings, preferences, and similarities, ideal for AI-driven recommendation systems.

an intelligent book recommendation system using llms and python. vibe code-free.

📖✨ 독서 감상을 AI가 분석해 비슷한 분위기의 책을 추천해주는 모바일 앱 — pgvector 임베딩 × 카카오 도서 검색 하이브리드 🤖📚