This project is about Building a reliable Book Recommendation system through datasets provided,
Books recommendation system based on a hybrid approach of both content-based and collaborative filtering.
The project used Python to create a personalized book recommendation system that analyzed users' past ratings on books to identify their preferences and patterns and suggested books that the user is likely to enjoy but has not read yet.
📚 Book Recommendation website where recommendations are generated by Collaborative Filtering.
This repository is based on the lecture '고객데이터와 딥러닝을 활용한 추천시스템 구현'
Comparison of various deep neural networks for recommendation systems
book recommendation engine
Item-based collaborative filtering makes recommendations based on user-product interactions in the past.
This is my Final Year Project. It is a web-based Restaurant Recommender System using Collaborative Filtering and Content Based Filtering techniques.
A recommendation engine for an e-commerce website using collaborative filtering
A book recommender system is a type of a type of recommendation system where we have to recommend similar books to the reader based on his interest. In this project, we will implement the populartity based recommender system and collabrative based recommender system to build a book recommender system.
Book Recommendation System - Unsupervised
Book Recommendation System - Popularity Based and Collaborative Filtering Based
This repository consists of code and datasets used to built a book recommender system using collaborative filtering.
It's a website that recommends books from database to users based on ratings given by other users. Two recommender models are built viz. 1) Popularity Based Recommender 2) Using Collaborative Filtering Algorithm
This is a collaborative filtering-based recommendation engine for a music streaming service
Machine Learning in R
JavaScript implementation of Collaborative Filtering
Movie recommendation system based on Collaborative filtering using Apache Spark
Recommend products that Big Bazaar customers are most probable to buy in next one month.
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