A unified, comprehensive and efficient recommendation library

Sequential model-based optimization with a `scipy.optimize` interface

BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

Recommender Systems Paperlist that I am interested in

Code for CIKM2020 "S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization"

Sequential deep matching model for recommender system at Alibaba

A highly-modularized and recommendation-efficient recommendation library based on PyTorch.

rec_pangu is a flexible open-source project for recommendation systems. It incorporates diverse AI models like ranking algorithms, sequence recall, multi-interest models, and graph-based techniques. Designed for both beginners and advanced users, it enables rapid construction of efficient, custom recommendation engines.

Must-read Papers for Recommender Systems (RS)

Code for paper "EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning"

Several sequential recommended models implemented by tenosrflow1.x

Generative model for sequential recommendation based on Convolution Neural Networks (CNN))

A box of core libraries for recommendation model development

Official implementation of SIGIR'2021 paper: "Sequential Recommendation with Graph Neural Networks".

Code for CIKM2020 "S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization"

[TKDE 2022] The source code of "Dynamic Graph Neural Networks for Sequential Recommendation"

Codebase for KDD 2023 paper, Text Is All You Need: Learning Language Representations for Sequential Recommendation

[SIGIR'2023] "MAERec: Graph Masked Autoencoder for Sequential Recommendation"

[WWW'2023] "DCRec: Debiased Contrastive Learning for Sequential Recommendation"