Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data
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Updated
Aug 14, 2024 - Python
Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data
pyRecLab is a library for quickly testing and prototyping of traditional recommender system methods, such as User KNN, Item KNN and FunkSVD Collaborative Filtering. It is developed and maintained by Gabriel Sepúlveda and Vicente Domínguez, advised by Prof. Denis Parra, all of them in Computer Science Department at PUC Chile, IA Lab and SocVis Lab.
Python implementation of 'Scalable Recommendation with Hierarchical Poisson Factorization'.
(Python, R, C) Poisson matrix factorization (non-Bayesian version) (recommender systems)
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.
Source code for Self-Guided Learning to Denoise for Robust Recommendation. SIGIR 2022.
(ICTIR2020) "Unbiased Pairwise Learning from Biased Implicit Feedback"
Recommender system weighted regularized matrix factorization in python
(WSDM2020) "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"
Recommender System toolkit
Matrix Factorization based recsys in Golang. Because facts are more important than ever
A hybrid recommender system for suggesting CDN (content delivery network) providers to various websites
This is the repository for the Master of Science thesis titled "GAN-based Matrix Factorization for Recommender Systems".
(WSDM2020) "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"
(Python, R, C++) Library-agnostic evaluation framework for implicit-feedback recommender systems
A Pytorch Recommendation Framework with Implicit Feedback.
GitHub Mirror of RecPack: Experimentation Toolkit for Top-N Recommendation (see https://gitlab.com/recpack-maintainers/recpack)
Neural collaborative filtering (NCF) method is used for Microsoft MIND news recommendation dataset.
PyTorchCML is a library of PyTorch implementations of matrix factorization (MF) and collaborative metric learning (CML), algorithms used in recommendation systems and data mining.
Benchmarking different implementations of weighted-ALS matrix factorization
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