There are 2 repositories under book-recomendation topic.
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.
Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows
Book Recommendation Service
machine learning using python & tensorflow
Various Recommender System models tested on different datasets
Predicting new link, detecting communities on Amazon Product Co-Purchasing Network. Recommending books based on the underlying network related information. Demo Video Link -
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).
Movie and Book recommendation systems
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
Recommend books to the a user.
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.
This contains the code of Bharat Book Collection(a dummy book store for project) Back-end.
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.
Book Recommendation | Collaborative Filtering
Bookipedia is a book recommendation project that utilizes neural network embeddings and Wikipedia links to generate personalized book recommendations.
Book recommendation system through user-based collaborative filtering approach with Java, MySQL, JDBC, Book-Crossing dataset and ICEpdf library
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.
A LIMS(library information management system) which recommends book using apriori algorithm.
A Book Recommendation System based on Collaborative Filtering using Embedding layer to map the ratings given by similar users to the books.
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.
This is a Book Recommendation Suite that recommends a book based on the comments/reviews given by the other users, not number of stars, but textual understanding decides the "likability" of a particular book and then matching with the user's liking.
Machine Learning with Python solutions
This project aims to build a Collaborative Filtering-Based Recommender System for suggesting books to users.
Book Recommendation System
Movie-Book Cross-Domain Recommender System: Database based application that provides the user with recommendations of movies on the basis of the movies, books and genres explored and rated by him/her
This project is a rest-api that recommend books for user
In this project, with Pearson correlation, book recommendation algorithm builded to make recommendation between users by their ratings.
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A book recommendation system made using item-based collaborative filtering