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).
A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.
A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models
🛍 A real-world e-commerce dataset for session-based recommender systems research.
RecTools - library to build Recommendation Systems easier and faster than ever before
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
PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.
A Tensorflow based implicit recommender system
RecSys Library
2018 Spotify ACM RecSys Challenge 2'nd Place Solution
⚡️ Implementation of TRON: Transformer Recommender using Optimized Negative-sampling, accepted at ACM RecSys 2023.
GHRS: Graph-based hybrid recommendation system with application to movie recommendation
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