Open-source FastAPI recommendation engine for books • backend architecture • data handling • scalable systems

Java Spring Boot RAG & hybrid search system for graduation projects: Elasticsearch BM25, BGE-M3 vector search, reranker, evidence-grounded QA, book recommendation, Vue 3, offline evaluation.

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.

📚 AI-Powered English Book Recommender — Adaptive Bayesian vocabulary estimation, matching books with 80%+ coverage for Chinese learners

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.

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

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

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

📚 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.

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.

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

AI-powered book recommendation system using Google Books API

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

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

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

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.

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

Flask + MongoDB book recommendation app powered by an Apache Airflow Apriori pipeline