recommenders

Recommendation Systems

Two Tower

Gen-Rec

• GitHub - facebookresearch/generative-recommenders: Repository hosting code used to reproduce results in “Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations” (https://arxiv.org/abs/2402.17152).

Session Based / Sequential Recommenders

• [2309.07602] Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?

• [2308.07192] gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling

• [2308.06878] AutoSeqRec: Autoencoder for Efficient Sequential Recommendation

• GitHub - AIRI-Institute/Scalable-SASRec: This repository contains code for the paper “Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs”, RecSys ‘24

Articles

• Blueprints for recommender system architectures: 10th anniversary edition - AI, software, tech, and people, not in that order… by X

Code

• GitHub - pytorch/torchrec: Pytorch domain library for recommendation systems
• Microsoft Recommenders
• GitHub - Coder-Yu/SELFRec: An open-source framework for self-supervised recommender systems.
• RecBole v1.1.1 — RecBole 1.1.1 documentation
• GitHub - NVIDIA-Merlin/Transformers4Rec: Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
• GitHub - otto-de/TRON: ⚡️ Implementation of TRON: Transformer Recommender using Optimized Negative-sampling, accepted at ACM RecSys 2023.
• GitHub - asash/gSASRec-pytorch
• GitHub - AIM-SE/AC-TSR: Official code for paper “Attention Calibration for Transformer-based Sequential Recommendation”

Collaborative Filtering

• GitHub - HKUDS/DCCF: [SIGIR’2023] “DCCF: Disentangled Contrastive Collaborative Filtering”

Embeddings

• [2102.12029] Theoretical Understandings of Product Embedding for E-commerce Machine Learning
• Vector representation of products Prod2Vec: How to get rid of a lot of embeddings | by Alexander Golubev | Towards Data Science

Videos

• NVIDIA Recommender Systems Summit 2022

Tutorials

• https://github.com/NVIDIA-Merlin/publications/tree/main/tutorials/RecSys22tutorial