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

Welcome to the "Book Recommender System" project! This collaborative recommender system uses the K-Nearest Neighbors (KNN) algorithm to recommend books based on user preferences. Explore new books you'll love!

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).

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

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

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.

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.

A Book Recommendation System based on Collaborative Filtering using Embedding layer to map the ratings given by similar users to the books.

Machine Learning with Python solutions

Recommend books to the a user.

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

Book recommender command-line application.

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.

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

Book recommendation system through user-based collaborative filtering approach with Java, MySQL, JDBC, Book-Crossing dataset and ICEpdf library

Book Recommendation System

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.