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.
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Updated
May 25, 2023 - Python
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.
This repository contains the code for building movie recommendation engine.
A recommender system built for book lovers.
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
A repository for a machine learning project about developing a hybrid movie recommender system.
This is a book recommendation engine built using a hybrid model of Collaborative filtering, Content Based Filtering and Popularity Matrix.
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 supervisor
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"
Hybrid recommedation for talents
Recommendation engine with a .97 AUC achieved using clustering techniques to create user features. Data represents Olist marketplace transactions and was retrieved from kaggle.com.
Movie recommendation system based on hybrid recommender and clustering
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
A Content Based And A Hybrid Recommender System using content based filtering and Collaborative filtering
Recommends movies using Collaborative and Content based filtering techniques
A hybrid recommender system for suggesting CDN (content delivery network) providers to various websites
Amar deep architectures for hybrid recommenders with GNNs
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
Using hybrid recommender system with apriori algorithm, content-based and collaborative filtering method for predicting users interactions and then recommend them for users.
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.
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