Research on multi-role classification task of online mall based on heterogeneous graph neural network

Research Article
Open access

Research on multi-role classification task of online mall based on heterogeneous graph neural network

Hanying Wei 1*
  • 1 Dalian University of Technology    
  • *corresponding author 2779600137@qq.com
Published on 21 February 2024 | https://doi.org/10.54254/2755-2721/39/20230581
ACE Vol.39
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-303-6
ISBN (Online): 978-1-83558-304-3

Abstract

With the rapid development of e-commerce, online shopping malls have become an indispensable part of daily life. In order to better meet the needs of consumers, marketplace platforms need to accurately identify and categorize different user personas to provide personalized services and recommendations. In traditional role classification methods, basic information and behavioral data of users are typically used for classification. However, this approach often ignores the complex relationships between users and multiple heterogeneous data such as goods, reviews, social networks, and more. Therefore, we propose a new approach based on heterogeneous graphs to model different types of data in the form of graphs to better capture the connections between users and various elements in the marketplace. In this study, graph embedding technology is used to map nodes in heterogeneous graphs into low-dimensional vector spaces to capture similarities and relationships between nodes. Then, using the vector representation of these nodes, we can apply algorithms such as attention mechanisms for multi-role classification. Specifically, we use algorithms such as support vector machines to train classification models and use heterogeneous graph attention mechanisms to obtain the final feature representation of nodes. Experimental results show that our method shows significant advantages in multi-role classification tasks. Finally, the results of this study are discussed and summarized. We found that the classification model based on heterogeneous graph can effectively classify multiple roles in the online mall to provide personalized services and recommendations for the mall. At the same time, we also find that the construction of heterogeneous maps and the choice of graph embedding technology have important impacts on the classification results, which need further research and optimization. Therefore, multi-role task classification of online shopping malls based on heterogeneous graph neural networks is of great significance for improving the user experience and recommendation effect of online shopping malls, and also provides new ideas and methods for research in related fields.

Keywords:

Heterogeneous Graphs, Neural Networks, Task Recommendation, Role Classification

Wei,H. (2024). Research on multi-role classification task of online mall based on heterogeneous graph neural network. Applied and Computational Engineering,39,63-70.
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References

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[17]. J. Pei, K. Zhong, J. Li and Z. Yu, “PAC: Partial Area Clustering for Re-Adjusting the Layout of Traffic Stations in City’s Public Transport,” in IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 1251-1260, Jan. 2023, doi: 10.1109/TITS.2022.3179024.

Cite this article

Wei,H. (2024). Research on multi-role classification task of online mall based on heterogeneous graph neural network. Applied and Computational Engineering,39,63-70.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-303-6(Print) / 978-1-83558-304-3(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.39
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Li J, Li S, Cheng L, et al. BSAS: A Blockchain-Based Trustworthy and Privacy-Preserving Speed Advisory System[J]. IEEE Transactions on Vehicular Technology, 2022, 71(11): 11421-11430.

[2]. Mumtaz S, Lundqvist H, Huq K M S, et al. Smart Direct-LTE communication: An energy saving perspective[J]. Ad Hoc Networks, 2014, 13: 296-311.

[3]. Mumtaz S, Huq K M S, Radwan A, et al. Energy efficient interference-aware resource allocation in LTE-D2D communication[C]//2014 IEEE International Conference on Communications (ICC). IEEE, 2014: 282-287.

[4]. Liang Yan, Liu Chao, Liang Zhongxiong, Li Wentao. Aspect level sentiment analysis using fused multi attention neural networks [J]. Computer Engineering and Design, 2023,44 (03): 894-900. DOI: 10.16208/j.issn1000-7024.2023.03.035.

[5]. Y. Wang, M. Huang, X. Zhu, and L. Zhao, “Attention-based LSTM for aspect-level sentiment classification,” in Proc. Conf. Empirical Methods Natural Lang. Process., Nov. 2016, pp. 606–615.

[6]. C. R. Aydin and T. Gungor, “Combination of recursive and recurrent neural networks for aspect-based sentiment analysis using inter-aspect relations,” IEEE Access, vol. 8, pp. 77820–77832, 2020.

[7]. Y. Li, C. Yin, and S.-H. Zhong, “Sentence constituent-aware aspectcategory sentiment analysis with graph attention networks,” in Natural Language Processing and Chinese Computing, vol. 12430, 2020, pp. 815–827.

[8]. SANGEETHA K,PRABHA D. Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM[J]. Journal of Ambient Intelligence and Humanized Computing,2021,12(6):4117-4126.

[9]. YIN D,MENG T,CHANG K W. SentiBERT:a transferable transformer-based architecture for compositional sentiment semantics[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics,2020:3695-3706.

[10]. Grover A, Leskovec J. node2vec: Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016: 855-864.

[11]. Van der Maaten L, Hinton G. Visualizing data using t-SNE[J]. Journal of machine learning research, 2008, 9(11).

[12]. Schlichtkrull M, Kipf T N, Bloem P, et al. Modeling relational data with graph convolutional networks[C]//The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15. Springer International Publishing, 2018: 593-607.

[13]. Chen Y, Mishra P, Franceschi L, et al. Refactor gnns: Revisiting factorisation-based models from a message-passing perspective[J]. Advances in Neural Information Processing Systems, 2022, 35: 16138-16150.

[14]. Zhao T, Yang C, Li Y, et al. Space4hgnn: a novel, modularized and reproducible platform to evaluate heterogeneous graph neural network[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022: 2776-2789.

[15]. Ahn H, Yang Y, Gan Q, et al. Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks[J]. Advances in Neural Information Processing Systems, 2022, 35: 38436-38448.

[16]. Wang X, Ji H, Shi C, et al. Heterogeneous graph attention network[C]//The world wide web conference. 2019: 2022-2032.

[17]. J. Pei, K. Zhong, J. Li and Z. Yu, “PAC: Partial Area Clustering for Re-Adjusting the Layout of Traffic Stations in City’s Public Transport,” in IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 1251-1260, Jan. 2023, doi: 10.1109/TITS.2022.3179024.

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