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Book Recommendation System built for Book Lovers📖. Simply Rate ⭐ some books and get immediate recommendations🤩
This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.
Content based recommender system for books using NLP.
machine learning using python & tensorflow
Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows
Book Recommendation Service
Predicting new link, detecting communities on Amazon Product Co-Purchasing Network. Recommending books based on the underlying network related information. Demo Video Link -
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.
Various Recommender System models tested on different datasets
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!
Recommend books to the a user.
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).
Movie and Book recommendation systems
Bookipedia is a book recommendation project that utilizes neural network embeddings and Wikipedia links to generate personalized book recommendations.
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
This contains the code of Bharat Book Collection(a dummy book store for project) Back-end.
Book recommendation system through user-based collaborative filtering approach with Java, MySQL, JDBC, Book-Crossing dataset and ICEpdf library
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.
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