Bookipedia is a book recommendation project that utilizes neural network embeddings and Wikipedia links to generate personalized book recommendations.
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Updated
Mar 24, 2023 - CSS
Bookipedia is a book recommendation project that utilizes neural network embeddings and Wikipedia links to generate personalized book recommendations.
Recommend books to the a user.
Uses Google Books API to create a list of top 10 similar books based on a user-inputted prompt
This is a Basic but Strong Book Recommendation API made with Flask by HIRANMAY ROY using Kaggle Database
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 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 project aims to build a Collaborative Filtering-Based Recommender System for suggesting books to users.
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.
Book recommender command-line application.
📚 Book recommendation system that utilizes user-friendly collaborative filtering techniques to suggest personalized book recommendations.
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.
Movie and Book recommendation systems
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).
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.
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
Using large language models to build a semantic book recommender.
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