A unified, comprehensive and efficient recommendation library
-
Updated
Sep 5, 2024 - Python
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"
Add a description, image, and links to the sequential-recommendation topic page so that developers can more easily learn about it.
To associate your repository with the sequential-recommendation topic, visit your repo's landing page and select "manage topics."