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

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…

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

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

Content and Collaborative Filtering based book recommendation system

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

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

Build a book recommendation system webapp that best predicts the user interests and recommend the suitable books to them, using various approaches. Python Flask framework is used here.

📚A book recommendation and classification system as well as a simple image retrieval system, using the Goodreads dataset.

DL Recommendation System - Book Recommender

Collaborative filtering based book recommendation model deployed using flask

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

Project based on Collaborative filtering using KNN clustering on books dataset, along with Streamlit webapp

Comparison of Hybrid Book Recommender Systems: Matrix Factorization with Neural Networks vs. Neural Collaborative Filtering with Attention

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

This Project Book Recommendation System Develop Using Streamlit and It Contains User-Based Collaborative Filtering & Top Rating of Books.

To develop a Book Recommender System using collaborative filtering with k-NN using a dataset with 278,858 users and 1,149,780 book ratings.

Collaborative filter based recommendation system along with user searching pattern.

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.