CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based Recommendation

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成果类型：

期刊论文

作者：

Ma, Yutao;Wang, Zesheng*;Huang, Liwei;Wang, Jian

通讯作者：

Wang, Zesheng;Wang, J

作者机构：

[Wang, Jian; Wang, Zesheng; Wang, ZS; Wang, J; Ma, Yutao] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China.

[Ma, Yutao] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.

[Ma, Yutao] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.

[Huang, Liwei] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China.

[Huang, Liwei] Beijing Inst Remote Sensing, Beijing 100854, Peoples R China.

通讯机构：

[Wang, ZS; Wang, J ] W

Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China.

语种：

英文

关键词：

Session-based recommendationheterogeneous hypergraphshypergraph neural networksinformation lossadditional information

期刊：

ACM Transactions on the Web

ISSN：

1559-1131

年：

2024

卷：

期：

页码：

1–37

DOI：

10.1145/3626569

基金类别：

National Key Research and Development Program of China [2020AAA0107705]; National Science Foundation of China [61972292, 62006023]

机构署名：

本校为其他机构

院系归属：

计算机学院

摘要：

In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR appro...

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CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based Recommendation

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