This repository contains Deep Learning based articles , paper and repositories for Recommender Systems

A recommender engine built for a Bay Area online dating website to maximize the successful matches by introducing hybrid recommender system and reverse match technique.

This repository contains the core model we called "Collaborative filtering enhanced Content-based Filtering" published in our UMUAI article "Movie Genome: Alleviating New Item Cold Start in Movie Recommendation"

Hotel Recommendation system based on Content, Collaborative, Social Network Based Systems

A python based hybrid recommendation system built from scratch

This is the source code for my MSc thesis on Hybrid Recommendation Systems using Neural Networks.

A small neural net to recommend movies to the user

Repository related to the project of the Data Mining graduate course of University of Trento, academic year 2022/2023.

The goal of this project is to implement a Hybrid Recommender System that combines item-based and user-based recommendation methods to provide movie recommendations for a specific user. The system aims to offer a total of 10 movie recommendations by using both methods.

A implementation in Scala of CF, Content Based, Sequential and hybrid recommender systems for Spark

Ohara bookshelf is a smart books recommendation platform using machine learning algorithms and neural network.

🚗This GitHub repository hosts a project focused on car recommendations using content-based and collaborative filtering algorithms.

Sistema de Recomendação Híbrido desenvolvido no formato de WebService RESTful utilizando Spring Boot

The assignment comprises two main tasks: implementing LSH to identify similar businesses based on user ratings and developing various collaborative filtering recommendation systems to predict user ratings for businesses.

Implemented all the 3 major types of Recommendation Systems, namely, Item Based CF Recommendation System, Model-Based Recommendation System and Hybrid Recommendation System.

Recommendation bot

This project developed and optimized a hybrid recommendation system that processes over 450,000 training data points and 142,000 validation data points. The system combines user ratings, merchant details, and user reviews to predict users' ratings for restaurants they have not visited.

Trend Fitness is a web application dedicated to providing professional fitness advice which will include a range from fitness plans to diet plans catered to every individual needs. I believe that my web application will embark on a transformative journey towards a healthier lifestyle.