#计算机科学#This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
#计算机科学#A recommender system built for book lovers.
This repository contains the code for building movie recommendation engine.
#计算机科学#A repository for a machine learning project about developing a hybrid movie recommender system.
#计算机科学#A Hybrid recommendation engine built on deep learning architecture, which has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervi...
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
Hybrid recommedation for talents
#计算机科学#A Hybrid Recommendation system which uses Content embeddings and augments them with collaborative features. Weighted Combination of embeddings enables solving cold start with fast training and serving
#计算机科学#A Content Based And A Hybrid Recommender System using content based filtering and Collaborative filtering
This is a book recommendation engine built using a hybrid model of Collaborative filtering, Content Based Filtering and Popularity Matrix.
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
Movie recommendation system based on hybrid recommender and clustering
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 Reco...
Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
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.
Recommendation System Algorithm
#计算机科学#Auto encoders based recommendation system
A hybrid recommender system for suggesting CDN (content delivery network) providers to various websites
Comparison of performance evaluation of the baseline and hybrid recommendation systems using various metrics, to prove that hybrid systems perform better