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https://github.com/wubinzzu/multi-behavior-recommendation-papers

Multi-Behavior Recommendation-Papers
https://github.com/wubinzzu/multi-behavior-recommendation-papers
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Multi-Behavior Recommendation-Papers
Host: GitHub
URL: https://github.com/wubinzzu/multi-behavior-recommendation-papers
Owner: wubinzzu
Created: 2024-03-19T13:05:12.000Z (10 months ago)
Default Branch: main
Last Pushed: 2024-05-22T03:32:53.000Z (8 months ago)
Last Synced: 2024-05-22T04:32:16.015Z (8 months ago)
Size: 6.84 KB
Stars: 5
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
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README

        ### A Paper List for Multi-Behavior Recommendation

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*

#### Paper List

- `FCS(2023)`  BGNN_ Behavior-aware graph neural network for heterogeneous session-based recommendation.  **[GNN]**  **[[PDF](https://journal.hep.com.cn/fcs/EN/article/downloadArticleFile.do?attachType=PDF&id=33255)]** 

- `WSDM(2023)`  Knowledge Enhancement for Contrastive Multi-Behavior Recommendation.  **[Contrastive Learning]**  **[[PDF](https://arxiv.org/pdf/2301.05403.pdf)]** 

- `WWW(2023)`  Compressed Interaction Graph based Framework for Multi-behavior Recommendation.  **[GCN]**  **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3543507.3583312)]**  **[[code](https://github.com/MC-CV/CIGF)]** 

- `WWW(2023)`  Denoising and Prompt-Tuning for Multi-Behavior Recommendation.  **[GNN]**  **[[PDF](https://arxiv.org/pdf/2302.05862.pdf)]**  **[[code](https://github.com/zc-97/DPT)]** 

- `WWW(2023)`  Multi-Behavior Recommendation with Cascading Graph Convolution Networks.  **[GCN]**  **[[PDF](http://export.arxiv.org/pdf/2303.15720)]**  **[[code](https://github.com/SS-00-SS/MBCGCN)]** 

- `TKDE(2023)`  Multi-Behavior Sequential Recommendation with Temporal Graph Transformer.  **[GNN+Transformer]**  **[[PDF](https://arxiv.org/pdf/2206.02687.pdf)]**  **[[code](https://github.com/akaxlh/TGT)]** 

- `ArXiv(2023)`  MB-HGCN A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation.  **[GCN]**  **[[PDF](https://arxiv.org/pdf/2306.10679.pdf)]**  **[[code](https://github.com/MingshiYan/MB-HGCN)]** 

- `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](https://dl.acm.org/doi/pdf/10.1145/3580305.3599838)] [[code]( https://github.com/MC-CV/HPMR)]**

- `SIGIR(2023)`  Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment.   **[[PDF](https://arxiv.org/pdf/2305.05585.pdf)]**  **[[code](https://github.com/LiuXiangYuan/MBA)]** 

- `SIGIR(2023)`  Multi-behavior Self-supervised Learning for Recommendation.  **[GNN]**  **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3539618.3591734)]**  **[[code](https://github.com/Scofield666/MBSSL.git)]** 

- `CIKM(2023)`  Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation.  **[GCN]**  **[[PDF](https://arxiv.org/pdf/2308.04807.pdf)]**  **[[code](https://github.com/MC-CV/PKEF)]** 

- `TOIS(2023)`  Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation.  **[CF+GCN]**  **[[PDF](https://arxiv.org/pdf/2205.13128.pdf)]**  **[[code](https://github.com/MingshiYan/CRGCN)]** 

- `ArXiv(2023)`  A Survey on Multi-Behavior Sequential Recommendation.   **[[PDF](https://arxiv.org/pdf/2308.15701.pdf)]** 

- `AAAI(2023)`  Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation.  **[RNN]**  **[[PDF](https://arxiv.org/pdf/2301.12105.pdf)]** 

- `RecSys(2023)`  Multi-Relational Contrastive Learning for Recommendation.  **[CL]**  **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3604915.3608807)]**  **[[code](https://github.com/HKUDS/RCL)]**

- `DASFAA(2022)`  Multi-view Multi-behavior Contrastive Learning in Recommendation.  **[CL]**  **[[PDF](https://browse.arxiv.org/pdf/2203.10576)]**  **[[code](https://github.com/wyqing20/MMCLR)]**

- `WSDM(2022)`  Contrastive Meta Learning with Behavior Multiplicity for Recommendation.  **[CF+GNN]**  **[[PDF](https://arxiv.org/pdf/2202.08523.pdf)]**  **[[code](https://github.com/weiwei1206/CML.git)]** 

- `DASFAA(2022)`  Neural Multi-Task Recommendation from Multi-Behavior Data.  **[Multi-Task]**  **[[PDF](https://arxiv.org/pdf/2203.10576.pdf)]**  **[[code](https://github.com/wyqing20/MMCLR)]** 

- `DASFAA(2022)`  Multi-behavior Recommendation with Two-Level Graph Attentional Networks.  **[Transformer]** 

- `SIGIR(2022)`  Multi-Behavior Sequential Transformer Recommender.  **[Transformer]**  **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3477495.3532023)]**  **[code]** 

- `KDD(2022)`  Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation.  **[Transformer]**  **[[PDF](https://arxiv.org/pdf/2207.05584.pdf)]**  **[[code](https://github.com/yuh-yang/MBHT-KDD22)]** 

- `ArXiv(2022)`  Causal Intervention for Fairness in Multi-behavior Recommendation.   **[[PDF](https://arxiv.org/pdf/2209.04589.pdf)]** 

- `TNNLS(2022)`  Multi-Behavior Graph Neural Networks for Recommender System.  **[GNN]**  **[[PDF](https://arxiv.org/pdf/2302.08678.pdf)]**  **[[code](https://github.com/akaxlh/MBRec)]** 

- `TKDD(2022)`  MBN: Towards Multi-Behavior Sequence Modeling for Next Basket Recommendation.  **[[code](https://github.com/gybuay/MBN)]**

- `ICDE(2021)`  Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling.  **[GNN]**  **[[PDF](https://arxiv.org/pdf/2201.02307v1.pdf)]**  **[[code](https://github.com/akaxlh/GNMR)]** 

- `GeoInformatica(2021)`  Graph neural network based model for multi-behavior session-based recommendation.  **[GNN]**  **[[PDF](https://link.springer.com/content/pdf/10.1007/s10707-021-00439-w.pdf?pdf=button)]**  **[code]** 

- `SIGIR(2021)`  Graph Meta Network for Multi-Behavior Recommendation.  **[GNN]**  **[PDF]**  **[[code](https://github.com/akaxlh/MB-GMN)]** 

- `ArXiv(2021)`  Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation.  **[Transformer]**  **[[PDF](https://arxiv.org/pdf/2110.04000v1.pdf)]**  **[[code](https://github.com/akaxlh/KHGT)]** 

- `ICDM(2021)`  Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation.  **[GCL]**  **[[PDF](https://arxiv.org/pdf/2109.02859v1.pdf)]**  **[[code](https://github.com/Haoran-Young/HMG-CR)]** 

- `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](https://fi.ee.tsinghua.edu.cn/~gaochen/papers/TKDE2019-NMTR.pdf)]**  **[[code](https://github.com/fiblab)]** 

- `ICDE(2021)`  Sequential Recommendation on Dynamic Heterogeneous Information Network.  **[GNN]**

- `AAAI(2021)`  Graph Heterogeneous Multi-Relational Recommendation.  **[GNN]** **[[code](https://github.com/chenchongthu/GHCF)]**

- `SIGIR(2020)`  Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation.  **[RNN+GNN+MLP]**  **[[PDF](https://arxiv.org/pdf/2006.06922.pdf)]**  **[[code](https://github.com/ciecus/MKM-SR)]** 

- `SIGIR(2020)`  Multi-behavior Recommendation with Graph Convolutional Networks.  **[GCN]**  **[[PDF](http://staff.ustc.edu.cn/~hexn/papers/sigir20-MBGCN.pdf)]** 

- `SIGIR(2020)`  Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network.  **[Transformer]**  **[[PDF](https://arxiv.org/pdf/2110.04002.pdf)]**  **[[code](https://github.com/akaxlh/MATN)]** 

- `CIKM(2020)`  Multiplex Graph Neural Networks for Multi-behavior Recommendation.  **[GNN]**  **[[PDF](https://arxiv.org/pdf/2302.08678.pdf)]**  **[[code](https://github.com/akaxlh/MBRec)]** 

- `ICDE(2019)`  Neural Multi-Task Recommendation from Multi-Behavior Data.  **[NCF]**  **[[PDF](https://arxiv.org/pdf/1809.08161v2.pdf)]**  **[code]** 

- `TKDE(2016)`  A General Recommendation Model for Heterogeneous Networks.  **[GNN]**