This is a paper list for Multi-Behavior Recommendation,which also contains some related research areas.
Keywords: Recommend System, Multi-Behavior Recommendation, Multi-task Learning
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FCS(2023)
BGNN_ Behavior-aware graph neural network for heterogeneous session-based recommendation. [GNN] [PDF] -
WSDM(2023)
Knowledge Enhancement for Contrastive Multi-Behavior Recommendation. [Contrastive Learning] [PDF] -
WWW(2023)
Compressed Interaction Graph based Framework for Multi-behavior Recommendation. [GCN] [PDF] [code] -
WWW(2023)
Denoising and Prompt-Tuning for Multi-Behavior Recommendation. [GNN] [PDF] [code] -
WWW(2023)
Multi-Behavior Recommendation with Cascading Graph Convolution Networks. [GCN] [PDF] [code] -
TKDE(2023)
Multi-Behavior Sequential Recommendation with Temporal Graph Transformer. [GNN+Transformer] [PDF] [code] -
ArXiv(2023)
MB-HGCN A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation. [GCN] [PDF] [code] -
ICDM(2023)
Contrastive Learning-based Multi-behavior Recommendation with Semantic Knowledge Enhancement. [Contrastive Learning] -
ICDM(2023)
Variational Collective Graph AutoEncoder for Multi-behavior Recommendation. [GNN] -
SIGKDD(2023)
Hierarchical Projection Enhanced Multi-behavior Recommendation. [PDF] [code] -
SIGIR(2023)
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment. [PDF] [code] -
SIGIR(2023)
Multi-behavior Self-supervised Learning for Recommendation. [GNN] [PDF] [code] -
CIKM(2023)
Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation. [GCN] [PDF] [code] -
TOIS(2023)
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation. [CF+GCN] [PDF] [code] -
ArXiv(2023)
A Survey on Multi-Behavior Sequential Recommendation. [PDF] -
AAAI(2023)
Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation. [RNN] [PDF] -
RecSys(2023)
Multi-Relational Contrastive Learning for Recommendation. [CL] [PDF] [code] -
DASFAA(2022)
Multi-view Multi-behavior Contrastive Learning in Recommendation. [CL] [PDF] [code] -
WSDM(2022)
Contrastive Meta Learning with Behavior Multiplicity for Recommendation. [CF+GNN] [PDF] [code] -
DASFAA(2022)
Neural Multi-Task Recommendation from Multi-Behavior Data. [Multi-Task] [PDF] [code] -
DASFAA(2022)
Multi-behavior Recommendation with Two-Level Graph Attentional Networks. [Transformer] -
SIGIR(2022)
Multi-Behavior Sequential Transformer Recommender. [Transformer] [PDF] [code] -
KDD(2022)
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation. [Transformer] [PDF] [code] -
ArXiv(2022)
Causal Intervention for Fairness in Multi-behavior Recommendation. [PDF] -
TNNLS(2022)
Multi-Behavior Graph Neural Networks for Recommender System. [GNN] [PDF] [code] -
TKDD(2022)
MBN: Towards Multi-Behavior Sequence Modeling for Next Basket Recommendation. [code] -
ICDE(2021)
Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling. [GNN] [PDF] [code] -
GeoInformatica(2021)
Graph neural network based model for multi-behavior session-based recommendation. [GNN] [PDF] [code] -
SIGIR(2021)
Graph Meta Network for Multi-Behavior Recommendation. [GNN] [PDF] [code] -
ArXiv(2021)
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. [Transformer] [PDF] [code] -
ICDM(2021)
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation. [GCL] [PDF] [code] -
ICDM(2021)
Composition-Enhanced Graph Collaborative Filtering for Multi-behavior Recommendation. [GCF] [PDF] [code] -
TKDE(2021)
Learning to Recommend With Multiple Cascading Behaviors. [CF] [PDF] [code] -
ICDE(2021)
Sequential Recommendation on Dynamic Heterogeneous Information Network. [GNN] -
AAAI(2021)
Graph Heterogeneous Multi-Relational Recommendation. [GNN] [code] -
SIGIR(2020)
Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation. [RNN+GNN+MLP] [PDF] [code] -
SIGIR(2020)
Multi-behavior Recommendation with Graph Convolutional Networks. [GCN] [PDF] -
SIGIR(2020)
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network. [Transformer] [PDF] [code] -
CIKM(2020)
Multiplex Graph Neural Networks for Multi-behavior Recommendation. [GNN] [PDF] [code] -
ICDE(2019)
Neural Multi-Task Recommendation from Multi-Behavior Data. [NCF] [PDF] [code] -
TKDE(2016)
A General Recommendation Model for Heterogeneous Networks. [GNN]