基于异质图表达学习的跨境电商推荐模型

张瑾; 朱桂祥; 王宇琛; 郑烁佳; 陈镜潞

doi:10.11999/JEIT211524

基于异质图表达学习的跨境电商推荐模型

doi: 10.11999/JEIT211524

1.
江苏开放大学设计学院南京 210036
2.
南京财经大学江苏省电子商务重点实验室南京 210003

基金项目: 国家自然科学基金(91646204, 71372188)，江苏省国际创新合作项目(BZ2020008)

详细信息

作者简介:
张瑾：女，1984年生，硕士，实验师，研究方向为推荐系统、深度学习

朱桂祥：男，1988年生，博士，讲师，研究方向为商务智能，推荐系统

通讯作者:
朱桂祥　zgx881205@gmail.com

¹⁾ https://huggingface.co/docs/transformers/model_doc/bert
²⁾ https://pypi.org/project/gensim/
中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2021-12-16
- 修回日期: 2022-04-03
- 网络出版日期: 2022-04-20
- 刊出日期: 2022-11-14

The Recommender System of Cross-border E-commerce Based on Heterogeneous Graph Neural Network

1.
School of Design, Jiangsu Open University, Nanjing 210036, China
2.
Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing 210003, China

Funds: The National Natural Science Foundation of China (91646204, 71372188), The International Innovation Cooperation Project of Jiangsu Province (BZ2020008)

摘要

摘要: 跨境电商产品推荐已经成为电子商务领域新兴的研究议题之一。由于电商产品信息复杂多样、“用户-产品”关联矩阵极为稀疏并且冷启动问题突出，因此传统的协同过滤推荐模型很难奏效。而改进的深度协同过滤模型，只考虑了用户对产品的“显式”和“隐式”的反馈信息，忽视了由用户与项目组成的图结构信息，推荐性能很难满足平台和用户的要求。为了解决这些难题，该文提出基于异质图表达学习的图神经网络模型(HGNR)用于个性化的跨境电商产品推荐，该模型具有2个显著的优势：(1) 构造“用户-产品-主题”3部图作为模型的输入，通过图卷积神经网络(GCN)在异质图上进行高质量信息传播和聚合；(2)能够获取高质量的用户和产品表征向量，实现了用户和产品复杂交互关系的建模。在真实的跨境电商订单数据集上的实验结果表明，HGNR模型不仅在推荐性能上表现出色，还能有效提升冷启动用户的推荐准确率，与9种推荐基准算法相比，HGNR在评价指标HitRate@10, Item-coverage@10, MRR@10上至少提升了3.33%, 0.91%, 0.54%。
- 推荐系统 /
- 图神经网络 /
- 异质图神经网络 /
- 冷启动推荐 /
- 跨境电商
Abstract: Cross-border e-commerce products recommendation has become one of the emerging researching topics in the field of e-commerce. Due to the diversity and complexity of e-commerce product information, the “user-item” correlation matrix is extremely sparse and the cold start problem is prominent. As a result, the traditional collaborative filtering model seems to be malfunctional. Meanwhile, the improved recommendation model based on collaborative filtering or matrix factorization only considers the explicit and implicit feedback information of the users to the products, while ignoring the graph structure information composed of users and items, so that the recommendation performance is difficult to meet the requirements of the platform and users. To tackle these issues, a recommender system of cross-border e-commerce based on heterogeneous graph neural network, named Heterogeneous Graph Neural network Recommender system (HGNR), is proposed in this paper. The model has two significant advantages: (1) the three-part graph is used as input, and high-quality information dissemination and aggregation are carried out on heterogeneous graphs through Graph Convolutional neural Network (GCN); (2) high-quality user and product representation vectors can be obtained, and realize the modeling of the complex interaction between users and products. Experimental results on real cross-border e-commerce order data sets show that HGNR not only owns the superior performance, but also can effectively improve the recommendation accuracy of cold-start users. Compared with nine baseline methods for recommendation, HGNR achieves improvements of at least 3.33%, 0.91%, and 0.54% on evaluation metrics of HitRate@10, Item-coverage@10 and MRR@10.
- Recommendation system /
- Graph Neural Network (GNN) /
- Heterogeneous Graph Neural Network (HGNN) /
- Cold start recommendation /
- Cross-border e-commerce
¹⁾ https://huggingface.co/docs/transformers/model_doc/bert

²⁾ https://pypi.org/project/gensim/

HTML全文

图 1 “用户-产品-主题”3部图

下载: 全尺寸图片幻灯片

图 2 HGNR模型框架图

下载: 全尺寸图片幻灯片

图 3 Dropout对HGNR的影响

下载: 全尺寸图片幻灯片

图 4 针对冷启动用户的推荐性能

下载: 全尺寸图片幻灯片

表 1 总体性能比较(%)

模型	HR@3	Item-c@3	MRR@3	HR@5	Item-c@5	MRR@5	HR@10	Item-c@10	MRR@10
POP	2.90	1.85	62.33	8.97	3.09	34.23	30.77	6.17	21.19
ICF	22.92	91.98	78.67	27.52	93.83	69.38	33.33	94.44	59.59
UCF	24.97	79.01	77.17	29.08	85.19	69.52	33.49	93.21	62.11
SVD	7.88	65.73	61.14	12.34	78.86	50.07	18.26	86.15	45.53
NMF	9.37	68.71	63.55	14.51	79.18	53.11	20.06	85.75	51.36
CDL	29.03	88.21	76.54	35.23	90.48	70.97	38.55	93.32	63.42
DeepFM	30.67	89.45	79.98	34.78	90.67	71.05	40.76	93.47	62.71
NGCF	42.65	90.28	80.14	46.87	91.87	72.05	50.87	94.21	65.37
NGPR	46.79	91.23	80.67	50.31	92.85	74.67	54.61	95.91	66.07
HGNR	48.12	92.21	82.24	51.65	94.56	74.29	56.43	96.78	66.43
注: HR和Item-c分别代表HitRate和Item-coverage

下载: 导出CSV

表 2 GNN网络层数对推荐性能的影响(%)

模型	HitRate@10	Item-c@10	MRR@10
HGNR-1 GNN Layer	51.27	93.84	62.18
HGNR-2 GNN Layer	56.43	96.78	66.43
HGNR-3 GNN Layer	53.91	94.63	63.73

下载: 导出CSV

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