recsys

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

Survey: A collection of AWESOME papers and resources on the large language model (LLM) related recommender system topics.

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.

Repository hosting code for "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" (https://arxiv.org/abs/2402.17152).

LensKit recommender toolkit.

Compilation of high-profile real-world examples of failed machine learning projects

A collection of resources for Recommender Systems (RecSys)

A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.

Recommendation System using ML and DL

⚠️ [ARCHIVED] This version has been archived as of october 2024 and will not be updated anymore, please refer to the README for a link to the new version. This is the official repository for the Recommender Systems course at Politecnico di Milano.

RecTools - library to build Recommendation Systems easier and faster than ever before

🛍 A real-world e-commerce dataset for session-based recommender systems research.

A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models

Python recommendation toolkit

A general purpose recommender metrics library for fair evaluation.

Merlin Models is a collection of deep learning recommender system model reference implementations

Recommendations at "Reasonable Scale": joining dataOps with recSys through dbt, Merlin and Metaflow

[IJAIT 2021] MABWiser: Contextual Multi-Armed Bandits Library

Course on Recommender Systems conducted at the Faculty of Computer Science, National Research University - Higher School of Economics. Academic year 2024/2025.

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.