1College of Information, Liaoning University, Shenyang 110036, China 2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
[Objective] This paper proposes a new algorithm based on multi-layer information fusion in an e-commerce heterogeneous network, aiming to improve the accuracy of user community division. [Methods] First, we conducted hierarchical processing of the e-commerce heterogeneous networks and constructed user node embeddings based on different relationship types. Then, we merged users of different layers and obtained their embedding characterization in e-commerce heterogeneous networks. Third, we used the objective function to optimize the relevant parameters of the user nodes. Finally, we clustered these users with an improved K-means algorithm, and created the reasonable community division. [Results] The NMI and Sim@5 indicators of the proposed algorithm were 6.4% and 1.7% higher than the existing algorithms based on DeepWalk, Node2Vec, and GCN. The model effectively characterized user nodes and accurately divided their communities. [Limitations] We did not examine the time information and noise points from the heterogeneous network. [Conclusions] The proposed algorithm could improve the performance of friend prediction, group recommendation and other applications.
冯勇, 徐文韬, 王嵘冰, 徐红艳, 张永刚. 电商异构网络中基于多层信息融合的用户社区划分算法*[J]. 数据分析与知识发现, 2022, 6(5): 89-98.
Feng Yong, Xu Wentao, Wang Rongbing, Xu Hongyan, Zhang Yonggang. User Community Partition Based on Multi-layer Information Fusion in E-commerce Heterogeneous Network. Data Analysis and Knowledge Discovery, 2022, 6(5): 89-98.
Valdeolivas A, Tichit L, Navarro C, et al. Random Walk with Restart on Multiplex and Heterogeneous Biological Networks[J]. Bioinformatics, 2018, 35(3): 497-505.
doi: 10.1093/bioinformatics/bty637
[2]
Bagavathi A, Krishnan S. Multi-Net: A Scalable Multiplex Network Embedding Framework[C]// Proceedings of the 7th International Conference on Complex Networks and Their Applications. 2019: 119-131.
( Chen Wenjie, Wen Yi, Yang Ning. Fuzzy Overlapping Community Detection Algorithm Based on Node Vector Representation[J]. Data Analysis and Knowledge Discovery, 2021, 5(5): 41-50.)
( Ye Zhonglin, Cao Rong, Zhao Haixing, et al. Link Prediction Based on Matrix Factorization for DeepWalk[J]. Application Research of Computers, 2020, 37(2): 424-429.)
( Wang Wentao, Wu Lintao, Huang Ye, et al. Link Prediction Model Based on Densely Connected Convolutional Network[J]. Journal of Computer Applications, 2019, 39(6): 1632-1638.)
( Ge Yao, Chen Songcan. Graph Convolutional Network for Recommender Systems[J]. Journal of Software, 2020, 31(4): 1101-1112.)
[7]
Zhang H M, Qiu L W, Yi L L, et al. Scalable Multiplex Network Embedding[C]// Proceedings of the 27th International Joint Conference on A.pngicial Intelligence. 2018: 3082-3088.
[8]
Shi C, Li Y T, Zhang J W, et al. A Survey of Heterogeneous Information Network Analysis[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(1): 17-37.
doi: 10.1109/TKDE.2016.2598561
[9]
Tang L, Liu H. Uncovering Cross-Dimension Group Structures in Multi-dimensional Networks[C]// Proceedings of the 2009 SDM Workshop on Analysis of Dynamic Networks. 2009: 568-575.
[10]
Papalexakis E E, Akoglu L, Ience D. Do More Views of a Graph Help? Community Detection and Clustering in Multi-Graphs[C]// Proceedings of the 16th International Conference on Information Fusion. IEEE, 2013: 899-905.
[11]
Boutemine O, Bouguessa M. Mining Community Structures in Multidimensional Networks[J]. ACM Transactions on Knowledge Discovery from Data, 2017, 11(4): 1-36.
( Zhang Yihao, Zhu Xiaofei, Xu Chuanyun, et al. Hybrid Recommendation Approach Based on Deep Sentiment Analysis of User Reviews and Multi-View Collaborative Fusion[J]. Chinese Journal of Computers, 2019, 42(6): 1316-1333.)
[13]
Cen Y K, Zou X, Zhang J W, et al. Representation Learning for Attributed Multiplex Heterogeneous Network[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 1358-1368.
[14]
You Q Z, Jin H L, Wang Z W, et al. Image Captioning with Semantic Attention[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2016: 4651-4659.
[15]
Veličković P, Fedus W, Hamilton W L, et al. Deep Graph Infomax[OL]. arXiv Preprint, arXiv: 1809.10341.
[16]
McGill W. Multivariate Information Transmission[J]. Transactions of the IRE Professional Group on Information Theory, 1954, 4(4): 93-111.
doi: 10.1109/TIT.1954.1057469
( Ding Yi, Yang Jian. Comparison Between Euclidean Distance and Standardized Euclidean Distance in K-Nearest Neighbor Algorithm[J]. Computer Engineering & Software, 2020, 41(10): 135-136.)
[19]
Park C, Kim D, Han J W, et al. Unsupervised Attributed Multiplex Network Embedding[J]. Proceedings of the AAAI Conference on A.pngicial Intelligence, 2020, 34(4): 5371-5378.
[20]
Wang X, Ji H Y, Shi C, et al. Heterogeneous Graph Attention Network[C]// Proceedings of the World Wide Web Conference. ACM, 2019: 2022-2032.
[21]
He R N, McAuley J. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering[C]// Proceedings of the 25th International Conference on World Wide Web. 2016: 507-517.
[22]
Bennett J, Lanning S. The Netflix Prize[C]// Proceedings of KDD Cup and Workshop 2007.ACM Press, 2007: 35-38.
[23]
Zhang Y, Pang L, Shi L,et al. Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform[OL]. arXiv Preprint, arXiv: 1408.6515.
[24]
Byers J W, Mitzenmacher M, Zervas G. The Groupon Effect on Yelp Ratings: A Root Cause Analysis[C]// Proceedings of the 13th ACM Conference on Electronic Commerce. 2012: 248-265.
[25]
Dong Y X, Chawla N V, Swami A. MetaPath2Vec: Scalable Representation Learning for Heterogeneous Networks[C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017: 135-144.
( Xu Jinghang, Zuo Wanli, Liang Shining, et al. Causal Relation Extraction Based on Graph Attention Networks[J]. Journal of Computer Research and Development, 2020, 57(1): 159-174.)
[27]
Chen W J, Gu Y L, Ren Z C, et al. Semi-Supervised User Profiling with Heterogeneous Graph Attention Networks[C]// Proceedings of the 28th International Joint Conference on A.pngicial Intelligence. 2019: 2116-2122.
