This project is about Building a reliable Book Recommendation system through datasets provided,

😎 This repository contains some recommendation algorithms powered by AI.

Examples of different types of recommender systems.

Books recommendation system based on a hybrid approach of both content-based and collaborative filtering.

I built recommender systems for recommending products to user using Model-based recommendation system.

I created movie recommender system using content based filtering.

Collaborative filtering using SVD

This repository contains a Python script mf.py that implements Matrix Factorization for collaborative filtering. Collaborative filtering is a technique used in recommendation systems to predict user preferences by collecting information from many users. Matrix Factorization is one of the popular methods used in collaborative filtering.

A movie recommendation system, or a movie recommender system, is an ML-based approach to filtering or predicting the users' film preferences based on their past choices and behavior.

Built a movie recommender system using Movielens dataset using both content-based filtering approach and collaborative filtering method.

Movie Recommendation System using the 10M MovieLens dataset

A recommendation engine for an e-commerce website using collaborative filtering

A web app to recommend movies based on user input

book recommendation engine

3rd Year: 1st - 92. A Novel Context Aware Restaurant Recommender System Using Content-Boosted Collaborative Filtering (CACBCF).

Movie Recommendation System is an R project to enhance your Machine Learning knowledge. It is simply a recommendation system that provides consumers with various suggestions based on their history and interests.

Recommend movies to users by review history based on KNN and K-means

MovieLens 100K and MovieLens 1M recommender system

Recommender Systems Project

Demo is available at https://huggingface.co/spaces/quyanh/Book-Recommender-System