Collaborative filtering based book recommendation system

Powerful book recommender using Large Language Models (LLMs) and semantic vector search. It analyzes book descriptions to recommend contextually and emotionally similar titles. Includes zero-shot classification and an interactive Gradio interface for seamless user experience.

This project showcases the top 50 popular books based on user ratings and reviews. Users can input a book title, and the system recommends 4 related books based on similarity. The Flask web interface displays book titles, authors, ratings, and images for easy exploration.

AI-powered book recommendation chatbot using Gemini API. Sign in with Google, chat naturally, and discover books tailored to your interests. Built with Next.js, Tailwind, and NextAuth.

Uses Google Books API to create a list of top 10 similar books based on a user-inputted prompt

A Flask-based book recommendation system that suggests similar books using collaborative filtering and precomputed similarity scores.

A smart recommendation system that uses Twitter Sentiment Analysis and Genre Mapping to suggest books and movies tailored to a user’s emotional tone.

Using large language models to build a semantic book recommender.

Book Reviews App lets users register, log in, view book details, leave reviews, and manage profiles. Admins can approve reviews and control book content on the home page.

📚 Book recommendation system that utilizes user-friendly collaborative filtering techniques to suggest personalized book recommendations.

This project develops a Book Recommendation System using collaborative filtering with the Nearest Neighbors (NN) algorithm. The system recommends books by identifying similarities in a user-item matrix, suggesting titles that align with a user's preferences based on historical interactions.

This is a Basic but Strong Book Recommendation API made with Flask by HIRANMAY ROY using Kaggle Database

Book recommender command-line application.

We are proud to introduce our new book recommendation system, book.io. This system uses the user-to-user collaborative filtering model to recommend books to users based on their preferences and ratings.

MoodRiser is a web application created during a 24-hour hackathon at the CodeForAll Fullstack Programming Bootcamp. Utilizing HTML, CSS, JavaScript, Python with Flask, and various APIs including Spotify and Google Books, and OpenAI, this SPA helps users manage their emotions through personalized content recommendations based on their current mood.

A dive into the View Transitions API: Explore its workflow, animations, room for improvements, advantages for both SPAs and MPAs and learn how to use the API on a Multi-Page Application (MPA).

This repository contains the source code of book recommendation system using collaborative filtering. The system recommends the books based on the similarities between user profiles