A Hierarchical Attention Network for Cross-Domain Group Recommendation

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Liang, Ruxia;Zhang, Qian;Wang, Jianqiang;Lu, Jie

作者机构：

[Liang, Ruxia] Univ Technol Sydney, Australian Artificial Intelligence Inst AAII, Fac Engn & Informat Technol, Decis Syst & E Serv Intelligent DeSI Res Lab, Ultimo, NSW 2007, Australia.

[Liang, Ruxia] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.

[Liang, Ruxia] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.

[Lu, Jie; Zhang, Qian] Univ Technol Sydney, Australian Artificial Intelligence Inst, Faulty Engn & Informat Technol, Ultimo, NSW 2007, Australia.

[Wang, Jianqiang] Cent South Univ, Sch Business, Changsha 410083, Peoples R China.

语种：

英文

关键词：

Recommender systems;Data models;Task analysis;Neural networks;Adaptation models;Social networking (online);Knowledge engineering;Cross-domain recommender systems (CDRSs);group recommender systems (GRSs);hierarchical attention network;recommender systems

期刊：

IEEE Transactions on Neural Networks and Learning Systems

ISSN：

2162-237X

年：

2024

卷：

期：

页码：

3859-3873

DOI：

10.1109/TNNLS.2022.3200480

基金类别：

10.13039/501100000923-Australian Research Council (ARC) under the Australian Laureate Fellowship (Grant Number: FL190100149) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 71871228) 10.13039/501100004543-China Scholarship Council

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本校为其他机构

摘要：

Many online services allow users to participate in various group activities such as online meeting or group buying, and thus need to provide user groups with services that they are interested. The group recommender systems (GRSs) emerge as required and provide personalized services for various online user groups. Data sparsity is an important issue in GRSs, since even fewer group-item interactions are observed. Moreover, the group and the group members have complex and mutual relationships with each other, which exacerbates the difficulty in modeling the preferences of both a group and its mem...

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A Hierarchical Attention Network for Cross-Domain Group Recommendation

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