Recommend books to the a user.

Bookipedia is a book recommendation project that utilizes neural network embeddings and Wikipedia links to generate personalized book recommendations.

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

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

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

사용자 선택 기반 도서 추천 웹사이트.

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.

This project aims to build a Collaborative Filtering-Based Recommender System for suggesting books to users.

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 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.

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.

Book recommender command-line application.

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

Movie and Book recommendation systems

Welcome to the "Book Recommender System" project! This collaborative-based filtering model uses cosine similarity to recommend books. It's not just a recommendation system; it's your personalized book guide.

In this project, with Pearson correlation, book recommendation algorithm builded to make recommendation between users by their ratings.

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.

A LIMS(library information management system) which recommends book using apriori algorithm.

The project utilizes data analysis to recommend books based on user reviews for a given input book. Additionally, it retrieves top-rated books based on customer ratings.