ACM MM'23: A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation

Experiments codes for WSDM '24 paper "MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems"

The implementation of Contrastive Meta Learning with Behavior Multiplicity forRecommendation

Official code implementation for ICDE 23 paper MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation

A PyTorch Library for Multi-Task Learning

Codes, data, and baselines for CIKM 2023 Long Paper "Dual Intents Graph Modeling for User-centric Group Discovery"

Code implementation of "HAMUR: Hyper Adapter for Multi-Domain Recommendation" in CIKM‘2023

implementation code for 'PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations' in SIGIR 2023

One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation (WSDM-2023)

The source code is for the paper: “Exploring False Hard Negative Sample in Cross-Domain Recommendation” accepted in Recsys 2023 by Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin and…

The source code is for the paper: “Triple Sequence Learning for Cross-domain Recommendation” accepted in TOIS by Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin and Jie Zhou.

[WSDM 2024 Oral] This is our Pytorch implementation for the paper: "Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation".

[SIGIR 2023 Oral] This is our Pytorch implementation for the paper: "Meta-optimized Contrastive Learning for Sequential Recommendation".

The repository of WWW23 paper: Cross-domain Recommendation with Behavioral Importance Perceptron

The code for the all-MLP based sequential recommender TriMLP.

Implementation of the paper "Contrastive Learning with Bidirectional Transformers for Sequential Recommendation".

⚡️ Implementation of TRON: Transformer Recommender using Optimized Negative-sampling, accepted at ACM RecSys 2023.

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

Contrastive Learning for Conversion Rate Prediction

Official code for paper "Attention Calibration for Transformer-based Sequential Recommendation"

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

A python library for user-friendly forecasting and anomaly detection on time series.

Implementation of the paper "Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation".

[ICLR 2020] Contrastive Representation Distillation (CRD), and benchmark of recent knowledge distillation methods

[ECIR 2024] Official repository for the paper titled "Self Contrastive Learning for Session-based Recommendation"

Code for WSDM 2022 paper, Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation.

A unified, comprehensive and efficient recommendation library