Connecting latent relationships over heterogeneous attributed network for recommendation
Recently, deep neural network models for graph-structured data have been demonstrated to be influential in recommendation systems. Graph Neural Network (GNN), which can generate high-quality embeddings by capturing graph-structured information, is convenient for the recommendation. However, most exi...
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| Published in | Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 14; pp. 16214 - 16232 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.11.2022
Springer Nature B.V |
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| Abstract | Recently, deep neural network models for graph-structured data have been demonstrated to be influential in recommendation systems. Graph Neural Network (GNN), which can generate high-quality embeddings by capturing graph-structured information, is convenient for the recommendation. However, most existing GNN models mainly focus on the homogeneous graph. They cannot characterize heterogeneous and complex data in the recommendation system. Meanwhile, it is challenging to develop effective methods to mine the heterogeneity and latent correlations in the graph. In this paper, we adopt Heterogeneous Attributed Network (HAN), which involves different node types as well as rich node attributes, to model data in the recommendation system. Furthermore, we propose a novel graph neural network-based model to deal with HAN for Recommendation, called HANRec. In particular, we design a component connecting potential neighbors to explore the influence among neighbors and provide two different strategies with the attention mechanism to aggregate neighbors’ information. The experimental results on two real-world datasets prove that HANRec outperforms other state-of-the-art methods. |
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| AbstractList | Recently, deep neural network models for graph-structured data have been demonstrated to be influential in recommendation systems. Graph Neural Network (GNN), which can generate high-quality embeddings by capturing graph-structured information, is convenient for the recommendation. However, most existing GNN models mainly focus on the homogeneous graph. They cannot characterize heterogeneous and complex data in the recommendation system. Meanwhile, it is challenging to develop effective methods to mine the heterogeneity and latent correlations in the graph. In this paper, we adopt Heterogeneous Attributed Network (HAN), which involves different node types as well as rich node attributes, to model data in the recommendation system. Furthermore, we propose a novel graph neural network-based model to deal with HAN for Recommendation, called HANRec. In particular, we design a component connecting potential neighbors to explore the influence among neighbors and provide two different strategies with the attention mechanism to aggregate neighbors’ information. The experimental results on two real-world datasets prove that HANRec outperforms other state-of-the-art methods. |
| Author | Wang, Yueyang Ye, Weihao Li, Xiuhua Fan, Qilin Duan, Ziheng |
| Author_xml | – sequence: 1 givenname: Ziheng surname: Duan fullname: Duan, Ziheng organization: School of Big Data and Software Engineering, Chongqing University, College of Control Science and Engineering, Zhejiang University – sequence: 2 givenname: Yueyang orcidid: 0000-0003-3210-0930 surname: Wang fullname: Wang, Yueyang email: yueyangw@cqu.edu.cn organization: School of Big Data and Software Engineering, Chongqing University – sequence: 3 givenname: Weihao surname: Ye fullname: Ye, Weihao organization: School of Big Data and Software Engineering, Chongqing University – sequence: 4 givenname: Qilin surname: Fan fullname: Fan, Qilin organization: School of Big Data and Software Engineering, Chongqing University – sequence: 5 givenname: Xiuhua surname: Li fullname: Li, Xiuhua organization: School of Big Data and Software Engineering, Chongqing University |
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| CitedBy_id | crossref_primary_10_1093_bioinformatics_btae339 crossref_primary_10_1007_s10489_022_04057_3 crossref_primary_10_1007_s10489_022_04215_7 crossref_primary_10_26599_TST_2021_9010081 crossref_primary_10_1016_j_ins_2024_121127 crossref_primary_10_1016_j_patrec_2021_12_008 crossref_primary_10_1093_bib_bbae096 |
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| SubjectTerms | Artificial Intelligence Artificial neural networks Computer Science Heterogeneity Machines Manufacturing Mechanical Engineering Neural networks Processes Recommender systems Structured data |
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| Title | Connecting latent relationships over heterogeneous attributed network for recommendation |
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