(Python, R, C++) Library-agnostic evaluation framework for implicit-feedback recommender systems
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Updated
Jun 18, 2024 - C++
(Python, R, C++) Library-agnostic evaluation framework for implicit-feedback recommender systems
(Python, R, C) Poisson matrix factorization (non-Bayesian version) (recommender systems)
Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
Python implementation of 'Scalable Recommendation with Hierarchical Poisson Factorization'.
This project implements a robust recommender system for book recommendations, leveraging ensemble methods, user-specific strategies, XGBoost, and extensive data preprocessing to achieve high performance in the Recommender System 2023 Challenge hosted by Kaggle for students of Politecnico di Milano's Recommender Systems course.
A Julia implementation of three different recommender systems based on the concept of Neural Collaborative Filtering.
GitHub Mirror of RecPack: Experimentation Toolkit for Top-N Recommendation (see https://gitlab.com/recpack-maintainers/recpack)
A Pytorch Recommendation Framework with Implicit Feedback.
Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data
This project is a recommendation system built with implicit ALS algorithm using Netflix UK's watch history data. It provides personalized movie recommendations and exposes a FastAPI API route for easy integration.
Implementation of various collaborative filtering methods for recommender systems with implicit feedback
(WSDM2020) "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"
(ICTIR2020) "Unbiased Pairwise Learning from Biased Implicit Feedback"
(WSDM2020) "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"
ecommerce recommendation
PyTorchCML is a library of PyTorch implementations of matrix factorization (MF) and collaborative metric learning (CML), algorithms used in recommendation systems and data mining.
Source code for Self-Guided Learning to Denoise for Robust Recommendation. SIGIR 2022.
Recommender system weighted regularized matrix factorization in python
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation, SIGIR 2021
A case study of the Netflix Prize solution where, given anonymous data of users and the ratings given to movies, the objective to provide recommendations to users for movies which they would like, based on their past activity and taste.
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