Composition-Enhanced Graph Collaborative Filtering for Multi-behavior Recommendation-Reference-Cited by-同舟云学术

Composition-Enhanced Graph Collaborative Filtering for Multi-behavior Recommendation

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Data Mining (ICDM)
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Author:

Wu Daqing,Luo Xiao,Ma Zeyu,Chen Chong,Wang Pengfei,Deng Minghua,Ma Jinwen

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9678506/9678989/09679060.pdf?arnumber=9679060

Reference26 articles.

1. Neural Multi-task Recommendation from Multi-behavior Data

2. Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

3. Multi-behavior Recommendation with Graph Convolutional Networks

4. Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

5. Relational learning via collective matrix factorization

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3. Auto Graph Filtering for Bundle Recommendation;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01

4. SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-behavior Prediction;ACM Transactions on the Web;2023-10-11

5. MGSF: Towards Multi-Graphs Semantic Fusion for Multi-behavior Recommendation;2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta);2022-12