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"

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

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

(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.

practice DELF, latent factor model based on id embedding where attention mechanism is applied

training module for latent factor models with implicit feedback