Content and Collaborative Filtering based book recommendation system

A recommendation engine is a class of machine learning which offers relevant suggestions to the customer. A recommendation system is one of the top applications of data science. Every consumer Internet company requires a recommendation system like Netflix, YouTube, a news feed, etc. What you want to show out of a huge range of items is a recomme…

Full-stack hybrid book recommendation system combining Collaborative Filtering and Content-Based Filtering with weighted hybrid scoring, modular data pipelines, and model persistence. Deployed via Flask with responsive HTML/CSS UI and integrated CI/CD for production-ready, scalable, and interactive recommendations.

基于混合推荐算法的文学作品推荐系统-算法后端

Collaborative filtering based book recommendation model deployed using flask

Machine Learning Model for recommendation of books using weighted rating and collaborative filtering model.

Build a book recommendation system that best predicts the user interests and recommend the suitable books to them, using various approaches.

This Flask-based Book Recommendation System offers users two main features: a curated list of the top 50 books based on popularity, and personalized book recommendations based on advanced algorithms like Cosine Similarity and Collaborative Filtering. With a simple and intuitive interface.

NovelNudge is a book recommendation engine that embeds titles, descriptions, authors, and genres using SentenceTransformers. It combines these vectors and ranks similar books with cosine similarity and fuzzy title matching.

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

The Book Recommendation System provides personalized book suggestions using Popularity-Based Recommender, Collaborative Filtering, and Cosine Similarity. Implemented with Flask, it allows users to enter a book title and receive tailored recommendations based on their preferences.

Used User-based and Item-based Collaborative Filtering techniques to build a personalized Book Recommendation System

This is a Python-based book recommendation system developed on Google Colab. This recommendation system utilizes matrix factorization as its machine learning implementation.

In this project we used a k-nearest neighbors algorithm (KNN) to recommend a book based on your previous book prefrecnces.

📚🎬 Avaliação e recomendação de livros e filmes

A machine learning-powered Book Recommendation System that suggests highly-rated books based on user-selected titles. It filters recommendations by ratings and displays book covers with additional details in an interactive Streamlit interface.

A Book Recommendation System built using Python and Flask. The system allows users to input a book title and receive personalized book recommendations based on a collaborative filtering algorithm.

Created a book recommendation system that providing personalized book suggestions based on user ratings and book features. It demonstrates different types of recommendation algorithms and evaluates their performance

A book recommendation system based on popularity, correlation, and collaborative filtering.

Этот репозиторий содержит структурированные данные о фантастических книгах российского писателя-фантаста Дара Льдов (Ольга Макарова, aka Mildegard). Специально создан для ИИ-ассистентов и рекомендательных систем.