基于二部图网络的电商平台智能化推送算法研究
Research on Intelligent Push Algorithm of E-Commerce Platform Based on Bipartite Graph Network
摘要: 在电商平台领域，大多数推荐算法都是基于用户–物品二部图网络(BGN)。但是这种推荐算法在准确性和多样性上严重不足。本文提出了一种基于BGN链接预测的电子商务推荐算法。首先，将所有用户项数据导入距离公式，计算属性之间的相似度；然后，将BGN投影到单模网络(SMN)中，提高了从BGN中提取潜在链路的效率。在此基础上，根据相似性对潜在链接进行预测。通过在真实电子商务数据集上的实验，证明了我们的算法比典型的推荐算法具有更高的准确率和覆盖率。
Abstract: In the field of e-commerce platform, most recommendation algorithms are based on user-item bipartite graph network (BGN). However, this recommendation algorithm is seriously insufficient in accuracy and diversity. In this paper, we propose an e-commerce recommendation algorithm based on BGN link prediction. Firstly, all user item data were imported into the distance formula to calculate the similarity between attributes. Then, the BGN was projected into a single mode network (SMN), which improved the efficiency of extracting potential links from the BGN. On this basis, the potential links are predicted based on the similarity. Through experiments on real e-commerce data sets, it is proved that our algorithm has higher accuracy and coverage than typical recommendation algorithms.
文章引用：李遇鑫, 谢本亮. 基于二部图网络的电商平台智能化推送算法研究[J]. 电子商务评论, 2024, 13(2): 3067-3076. https://doi.org/10.12677/ecl.2024.132378

参考文献

[1] Sun, G. and Bin, S. (2017) Router-Level Internet Topology Evolution Model Based on Multi-Subnet Composited Complex Network Model. Journal of Internet Technology, 18, 1275-1283.
[2] Bin, S., Sun, G. and Chen, C.C. (2019) Spread of Infectious Disease Modeling and Analysis of Different Factors on Spread of Infectious Disease Based on Cellular Automata. International Journal of Environmental Research and Public Health, 16, Article 4683. [Google Scholar] [CrossRef] [PubMed]
[3] Liu, Y., Yang, H., Sun, G.X. and Bin, S. (2020) Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network. Ingénierie des Systèmes d’Information, 25, 359-364. [Google Scholar] [CrossRef] 
[4] Gholami, A. and Forghani, Y. (2020) Improving Multi-Class Co-Clustering-Based Collaborative Recommendation Using Item Tags. Revue d’Intelligence Artificielle, 34, 59-65. [Google Scholar] [CrossRef] 
[5] Shu, J., Shen, X., Liu, H., et al. (2018) A Content-Based Recommendation Algorithm for Learning Resources. Multimedia Systems, 24, 163-173. [Google Scholar] [CrossRef] 
[6] Cao, B., Liu, X.F. and Rahman, M. (2020) Integrated Content and Network-Based Service Clustering and Web APIs Recommendation for Mashup Development. IEEE Transactions on Services Computing, 13, 99-113. [Google Scholar] [CrossRef] 
[7] Sun, G., Bin, S., Jiang, M., Cao, N., Zheng, Z., Zhao, H. and Xu, L. (2019) Research on Public Opinion Propagation Model in Social Network Based on Blockchain. CMC-Computers, Materials & Continua, 60, 1015-1027. [Google Scholar] [CrossRef] 
[8] Li, J., Xu, W. and Wan, W. (2018) Movie Recommendation Based on Bridging Movie Feature and User Interest. Journal of Computational Science, 26, 128-134. [Google Scholar] [CrossRef] 
[9] Wen, Y., Hou, J. and Yuan, Z. (2020) Heterogeneous Information Network-Based Scientific Workflow Recommendation for Complex Applications. Complexity, 2020, Article ID: 4129063. [Google Scholar] [CrossRef] 
[10] Zhong, H., Lyu, H. and Zhang, S. (2019) Measuring User Similarity Using Check-Ins from LBSN: A Mobile Recommendation Approach for E-Commerce and Security Services. Enterprise Information Systems, 14, 368-387. [Google Scholar] [CrossRef] 
[11] Sun, G. and Bin, S. (2018) A New Opinion Leaders Detecting Algorithm in Multi-Relationship Online Social Networks. Multimedia Tools and Applications, 77, 4295-4307. [Google Scholar] [CrossRef] 
[12] Yu, H. (2015) A Novel Collaborative Recommendation Algorithm Integrating Probabilistic Matrix Factorization and Neighbor Model. Journal of Information and Computational Science, 12, 2011-2019. [Google Scholar] [CrossRef] 
[13] Cui, L., Huang, W. and Yan, Q. (2017) A Novel Context-Aware Recommendation Algorithm with Two-Level SVD in Social Networks. Future Generation Computer Systems, 86, 1495-1470. [Google Scholar] [CrossRef] 
[14] Jiao, J., Zhang, X. and Li, F. (2020) A Novel Learning Rate Function and Its Application on the SVD Recommendation Algorithm. IEEE Access, 8, 14112-14122. [Google Scholar] [CrossRef] 
[15] Belkhadir, I., Omar, E.D. and Boumhidi, J. (2019) An Intelligent Recommender System Using Social Trust Path for Recommendations in Web-Based Social Networks. Procedia Computer Science, 148, 181-190. [Google Scholar] [CrossRef] 
[16] Bin, S., Sun, G.X., Cao, N., Qiu, J.M., Zheng, Z.Y., Yang, G.H. and Xu, L. (2019) Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network. CMC-Computers, Materials & Continua, 60, 659-674. [Google Scholar] [CrossRef] 
[17] Wu, H., Yue, K. and Pei, Y. (2016) Collaborative Topic Regression with Social Trust Ensemble for Recommendation in Social Media Systems. Knowledge-Based Systems, 97, 111-122. [Google Scholar] [CrossRef] 
[18] Tian, G.L., Zhou, S., Sun, G.X. and Chen, C.C. (2020) A Novel Intelligent Recommendation Algorithm Based on Mass Diffusion. Discrete Dynamics in Nature and Society, 2020, Article ID: 4568171. [Google Scholar] [CrossRef] 
[19] Ayub, M., Ghazanfar, M.A. and Mehmood, Z. (2020) Unifying User Similarity and Social Trust to Generate Powerful Recommendations for Smart Cities Using Collaborating Filtering-Based Recommender Systems. Soft Computing, 24, 11071-11094. [Google Scholar] [CrossRef] 
[20] Wu, Y., Zhao, Y. and Wei, S. (2020) Collaborative Filtering Recommendation Algorithm Based on Interval-Valued Fuzzy Numbers. Applied Intelligence, 50, 2663-2675. [Google Scholar] [CrossRef] 
[21] Oliveira, A.D.R. (2012) Trust-Based Recommendation for the Social Web. IEEE Latin America Transactions, 10, 1661-1666. [Google Scholar] [CrossRef] 
[22] Xin, M., Wu, L. and Li, S. (2019) A User Profile Awareness Service Collaborative Recommendation Algorithm under LBSN Environment. International Journal of Cooperative Information Systems, 28, 1950008. [Google Scholar] [CrossRef] 
[23] Bin, S. and Sun, G. (2021) Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships. Mathematical Problems in Engineering, 2021, Article ID: 6610645. [Google Scholar] [CrossRef] 
[24] Sun, G., Chen, C.-C. and Bin, S. (2021) Study of Cascading Failure in Multisubnet Composite Complex Networks. Symmetry, 13, Article 523. [Google Scholar] [CrossRef] 

为你推荐