This repo contains my practice and template code for all kinds of recommender systems using SupriseLib. More complex and hybrid Recommender Systems can build on top of these template codes.

Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity

Recommender system that applies a user-to-user collaborative filtering algorithm on the MAL dataset to recommend anime for users.

기본적인 Recommendation System을 갖춘 REST API 서버와, 이를 표현하기 위한 간단한 프론트엔드를 구현한 페이지입니다.

A Collaborative filtering recommendation system

This repository contains the source code and documentation for a Bachelor's thesis project that explores two different approaches to developing a movie recommendation system.

Projeto Final de Graduação - Engenharia de Computação UNICAMP 2019. Revisão de Ténicas em Sistemas de Recomendação.

In this project, we develop a fully functional web application for a library recommendation system. Users can register, create and manage profiles, write and manage blog reviews on their reading experiences, and share their writings with the world. The app includes a comprehensive UX/UI, encompassing all features from the original project vision.

MovieLens recommended system project

Código-fonte desenvolvido para implementação da parte prática referente dissertação apresentada como requisito parcial para a obtenção do grau de Mestre em Ciência da Computação.

Adaptive Systems project assignment at Universidad Politécnica de Madrid: Creating a basic Recommender System for movies with Python and Surprise library

Recommender System built using Python, Angular, Firebase & MySQL