Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data

Python implementation of 'Scalable Recommendation with Hierarchical Poisson Factorization'.

A Pytorch Recommendation Framework with Implicit Feedback.

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.

(WSDM2020) "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"

(WSDM2020) "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"

PyTorchCML is a library of PyTorch implementations of matrix factorization (MF) and collaborative metric learning (CML), algorithms used in recommendation systems and data mining.

GitHub Mirror of RecPack: Experimentation Toolkit for Top-N Recommendation (see https://gitlab.com/recpack-maintainers/recpack)

(ICTIR2020) "Unbiased Pairwise Learning from Biased Implicit Feedback"

Source code for Self-Guided Learning to Denoise for Robust Recommendation. SIGIR 2022.

This is the repository for the Master of Science thesis titled "GAN-based Matrix Factorization for Recommender Systems".

A set of matrix factorization techniques to provide recommendations for implicit feedback datasets.

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation, SIGIR 2021

Recommender system weighted regularized matrix factorization in python

Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi

Tools for development of recommendation systems in Python.

Code for Simple and effective recommendations using implicit feedback-aware factorization machines

Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.