基于超图表示学习和Transformer模型优化的知识感知推荐

doi:10.11996/JG.j.2095-302X.2025051050

图学学报 ›› 2025, Vol. 46 ›› Issue (5): 1050-1060.DOI: 10.11996/JG.j.2095-302X.2025051050

• 计算机图形学与虚拟现实 • 上一篇下一篇

基于超图表示学习和Transformer模型优化的知识感知推荐

左屿琪¹(), 张云峰¹(), 张秋悦², 徐英城³

¹ 山东财经大学计算机与人工智能学院，山东济南 250014
² 山东师范大学信息科学与工程学院，山东济南 250014
³ 山东财经大学管理科学与工程学院，山东济南 250014

收稿日期:2024-08-22 接受日期:2024-12-25 出版日期:2025-10-30 发布日期:2025-09-10
通讯作者:张云峰(1977-)，男，教授，博士。主要研究方向为图像处理、可视化数据分析等。E-mail：yfzhang@sdufe.edu.cn
第一作者:左屿琪(1998-)，女，硕士研究生。主要研究方向为推荐算法。E-mail：yqzuo@mail.sdufe.edu.cn
基金资助:
山东省自然科学基金(ZR2022MF245);山东省高等学校“青创人才引育计划”;山东省自然科学基金青年基金(ZR2023QF161);山东省自然科学基金青年基金(ZR2024QF158);山东省高等学校青年创新团队项目(2022KJ185)

Knowledge-aware recommendation based on hypergraph representation learning and Transformer model optimization

ZUO Yuqi¹(), ZHANG Yunfeng¹(), ZHANG Qiuyue², XU Yingcheng³

¹ School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan Shandong 250014, China
² School of Information Science and Engineering, Shandong Normal University, Jinan Shandong 250014, China
³ School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan Shandong 250014, China

Received:2024-08-22 Accepted:2024-12-25 Published:2025-10-30 Online:2025-09-10
First author：ZUO Yuqi (1998-), master student. Her main research interest covers recommendation algorithm. E-mail：yqzuo@mail.sdufe.edu.cn
Supported by:
Natural Science Foundation of Shandong Province(ZR2022MF245);Foundation items：Youth Talent Introduction and Cultivation Plan in Colleges and Universities of Shandong Province;Shandong Province Natural Science Fundation Youth Branch(ZR2023QF161);Shandong Provincial Youth Fund(ZR2024QF158);Youth Innovation Team in Colleges and universities of Shandong Province(2022KJ185)

摘要/Abstract

摘要：

基于知识图谱的推荐算法是近年来推荐系统领域的一个研究重点和热点，这主要是因为引入知识图谱，能够获得项目的辅助信息，从而极大地增强了推荐系统的能力，为用户带来更为精准和个性化的推荐体验。为此，提出一种基于超图表示学习和Transformer模型优化的知识感知推荐模型，利用超图在处理高阶关系上的独特优势，直接对用户与项目之间复杂的交互信息进行建模，从而极大地丰富了其交互信息。因局部图缺少用户与项目之间的全局交互信息，因此在局部图中构造全局超图；而非局部图存在冗余信息，在非局部图中构造非全局超图，从而捕获用户与项目之间更全面的交互信息。同时，利用Transformer模型的注意力机制增强用户节点与项目节点之间的协作关系，并从用户点击不感兴趣的项目且有噪声的用户交互数据中挖掘更有价值的偏好信息，优化用户与项目节点的嵌入，以缓解噪声干扰，提升对用户偏好的推荐性能。

Abstract:

The recommendation algorithm based on knowledge graphs has emerged as a significant research focus and hotspot in the field of recommender systems in recent years. The introduction of knowledge graphs, enables the acquisition of auxiliary information about items, thereby significantly enhancing the capabilities of recommendation systems and providing users with more precise and personalized recommendation experiences. In response to this trend, a knowledge-aware recommendation model optimized via hypergraph representation learning and the Transformer model was proposed. This model leveraged the unique advantages of hypergraphs in handling high-order relationships to directly model the complex interaction information between users and items, thereby greatly enriching their interaction information. Since local graphs lack global interaction information between users and items, global hypergraphs were constructed within local graphs. On the other hand, nonlocal graphs contain redundant information, so nonlocal hypergraphs were built to capture more comprehensive interaction information between users and items. Additionally, the attention mechanism of the Transformer model was employed to strengthen the collaboration between user nodes and item nodes, mining more valuable preference information from noisy user interaction data such as clicks on uninteresting items. This optimized the embeddings of user and item nodes, mitigating the impact of noise and enhancing the recommendation performance based on user preferences.

Key words: knowledge graph, knowledge-aware recommendation, hypergraph, Transformer, attention mechanism

中图分类号:

TP391.3
TP18

左屿琪, 张云峰, 张秋悦, 徐英城. 基于超图表示学习和Transformer模型优化的知识感知推荐[J]. 图学学报, 2025, 46(5): 1050-1060.

ZUO Yuqi, ZHANG Yunfeng, ZHANG Qiuyue, XU Yingcheng. Knowledge-aware recommendation based on hypergraph representation learning and Transformer model optimization[J]. Journal of Graphics, 2025, 46(5): 1050-1060.

图/表 6

参考文献 34

[1]	HE X N, LIAO L Z, ZHANG H W, et al. Neural collaborative filtering[C]// The 26th International Conference on World Wide Web. Geneva: International World Wide Web Conferences Steering Committee, 2017: 173-182.
[2]	李振波, 杨晋琪, 岳峻. 基于协同回归模型的矩阵分解推荐[J]. 图学学报, 2019, 40(6): 983-990. DOI
	LI Z B, YANG J Q, YUE J. Matrix factorization recommendation based on collaborative regression model[J]. Journal of Graphics, 2019, 40(6): 983-990 (in Chinese).
[3]	HE X N, ZHANG H W, KAN M Y, et al. Fast matrix factorization for online recommendation with implicit feedback[C]// The 39th International ACM SIGIR conference on Research and Development in Information Retrieval. New York: ACM, 2016: 549-558.
[4]	KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37.
[5]	PAZZANI M J, BILLSUS D. Content-based recommendation systems[M]// BRUSILOVSKYP, KOBSAA, NEJDLW. The Adaptive Web:Methods and Strategies of Web Personalization. Heidelberg: Springer, 2007: 325-341.
[6]	RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]// The 25th Conference on Uncertainty in Artificial Intelligence. Arlington: AUAI Press, 2009: 452-461.
[7]	WANG H W, ZHAO M, XIE X, et al. Knowledge graph convolutional networks for recommender systems[C]// The World Wide Web Conference. New York: ACM, 2019: 3307-3313.
[8]	王永贵, 陈书铭, 刘义海, 等. 结合超图对比学习和关系聚类的知识感知推荐算法[J]. 计算机科学与探索, 2024, 18(8): 2140-2155. DOI
	WANG Y G, CHEN S M, LIU Y H, et al. Knowledge-aware recommendation algorithm combining hypergraph contrast learning and relational clustering[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(8): 2140-2155 (in Chinese).
[9]	赵超. 采用集成学习的大规模推荐系统优化研究[C]// 第七届创新教育学术会议论文集. 太原: 山西省中大教育研究院, 2023: 105-106.
	ZHAO C. Research on optimization of large-scale recommendation system based on ensemble learning[C]// The Seventh Innovation Education Academic Conference Proceedings. Taiyuan: Shanxi Zhongda Institute of Education, 2023: 105-106 (in Chinese).
[10]	GUO G B, ZHANG J, THALMANN D. A simple but effective method to incorporate trusted neighbors in recommender systems[C]// The 20th International Conference on User Modeling, Adaptation, and Personalization. Cham: Springer, 2012: 114-125.
[11]	何柳, 安然, 刘姝妍, 等. 基于知识图谱的航空多模态数据组织与知识发现技术研究[J]. 图学学报, 2024, 45(2): 300-307. DOI
	HE L, AN R, LIU Z Y, et al. Research on knowledge graph-based aviation multi-modal data organization and discovery method[J]. Journal of Graphics, 2024, 45(2): 300-307 (in Chinese). DOI
[12]	TANG X Y, ZHOU J. Dynamic personalized recommendation on sparse data[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2895-2899.
[13]	HU Z D, XU G Q, ZHENG X, et al. SSL-SVD: semi- supervised learning--based sparse trust recommendation[J]. ACM Transactions on Internet Technology (TOIT), 2020, 20(1): 4.
[14]	WU C, LIU S, ZENG Z Y, et al. Knowledge graph-based multi-context-aware recommendation algorithm[J]. Information Sciences, 2022, 595: 179-194.
[15]	YU J L, YIN H Z, LI J D, et al. Self-supervised multi-channel hypergraph convolutional network for social recommendation[C]// The Web Conference 2021. New York: ACM, 2021: 413-424.
[16]	XIA L H, HUANG C, XU Y, et al. Hypergraph contrastive collaborative filtering[C]// The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2022: 70-79.
[17]	YERA R, CASTRO J, MARTÍNEZ L. A fuzzy model for managing natural noise in recommender systems[J]. Applied Soft Computing, 2016, 40: 187-198.
[18]	LIU Z, FENG X D, WANG Y C, et al. Self-paced learning enhanced neural matrix factorization for noise-aware recommendation[J]. Knowledge-Based Systems, 2021, 213: 106660.
[19]	WANG H W, ZHANG F Z, ZHAO M, et al. Multi-task feature learning for knowledge graph enhanced recommendation[C]// The World Wide Web Conference. New York: ACM, 2019: 2000-2010.
[20]	REN L J, LU J, SUO W. Multi-source knowledge embedding research of knowledge graph[C]// 2019 IEEE 3rd International Conference on Circuits, Systems and Devices. New York: IEEE Press, 2019: 163-166.
[21]	ZHANG F Z, YUAN N J, LIAN D F, et al. Collaborative knowledge base embedding for recommender systems[C]// The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 353-362.
[22]	OKURA S, TAGAMI Y, ONO S, et al. Embedding-based news recommendation for millions of users[C]// The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2017: 1933-1942.
[23]	GRAD-GYENGE L, KISS A, FILZMOSER P. Graph embedding based recommendation techniques on the knowledge graph[C]// Adjunct PUBLICATION of the 25th Conference on User Modeling, Adaptation and Personalization. New York: ACM, 2017: 354-359.
[24]	WU G, LI L L, LI X Y, et al. Graph embedding based real-time social event matching for EBSNs recommendation[J]. World Wide Web, 2022, 25(1): 335-356.
[25]	WANG H W, ZHANG F Z, WANG J L, et al. RippleNet: propagating user preferences on the knowledge graph for recommender systems[C]// The 27th ACM International Conference on Information and Knowledge Management. New York: ACM, 2018: 417-426.
[26]	WANG X, HUANG T, WANG D, et al. Learning intents behind interactions with knowledge graph for recommendation[C]// The Web Conference 2021. New York: ACM, 2021: 878-887.
[27]	ZHAO H, DU H, YANG S Q, et al. Rec-RN: user representations learning over the knowledge graph for recommendation systems[C]// The 4th International Conference on Machine Learning, Big Data and Business Intelligence. New York: IEEE Press, 2022: 228-233.
[28]	ZOU D, WEI W, MAO X L, et al. Multi-level cross-view contrastive learning for knowledge-aware recommender system[C]// The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2022: 1358-1368.
[29]	FENG Y F, YOU H X, ZHANG Z Z, et al. Hypergraph neural networks[C]// The 33rd AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2019: 3558-3565.
[30]	XIA X, YIN H Z, YU J L, et al. Self-supervised hypergraph convolutional networks for session-based recommendation[C]// The 35th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2021, 35(5): 4503-4511.
[31]	刘昺昊. 基于超图神经网络的推荐方法研究[D]. 苏州: 苏州大学, 2022.
	LIU B H. Research on recommendation method with hypergraph neural network[D]. Suzhou: Soochow University, 2022 (in Chinese).
[32]	YANG Z, XU L K, ZHAO L. Efbh: collaborative filtering model based on multi-hypergraph encoder[J]. IEEE Transactions on Consumer Electronics, 2023, 70(1): 2939-2948.
[33]	WEI Y W, LIU W Q, LIU F, et al. LightGT: a light graph transformer for multimedia recommendation[C]// The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2023: 1508-1517.
[34]	LIU J, SONG L Y, WANG G T, et al. Meta-HGT: metapath-aware HyperGraph transformer for heterogeneous information network embedding[J]. Neural Networks, 2023, 157: 65-76.

分类	具体项	Book-Crossing	MovieLens-1M	Last.FM
用户-项目交互	#用户	17860	6036	1872
	#项目	14967	2445	3846
	#交互	139746	753772	42346
知识图谱	#实体	77903	182011	9366
	#关系	25	12	60
	#元组	151500	1241996	15518
超参数设置	#迭代次数	1000	1000	1000
	#嵌入向量维数	64	64	64
	#超边数	128	128	128
	#学习率	0.005 0	0.010 0	0.000 1

分类	具体项	Book-Crossing	MovieLens-1M	Last.FM
用户-项目交互	#用户	17860	6036	1872
	#项目	14967	2445	3846
	#交互	139746	753772	42346
知识图谱	#实体	77903	182011	9366
	#关系	25	12	60
	#元组	151500	1241996	15518
超参数设置	#迭代次数	1000	1000	1000
	#嵌入向量维数	64	64	64
	#超边数	128	128	128
	#学习率	0.005 0	0.010 0	0.000 1

方法	Book-Crossing		MovieLens-1M		Last.FM
方法	AUC	F₁	AUC	F₁	AUC	F₁
BPRFM^[5]	0.649 3	0.610 8	0.883 8	0.789 3	0.713 4	0.640 6
CKE^[21]	0.675 6	0.620 2	0.900 8	0.797 2	0.721 6	0.643 1
RippleNet^[25]	0.719 1	0.645 0	0.911 5	0.827 1	0.770 2	0.694 8
KGCN^[7]	0.682 1	0.629 3	0.907 8	0.831 0	0.757 9	0.701 9
KGIN^[26]	0.721 5	0.661 9	0.912 5	0.841 3	0.808 1	0.731 3
HGNN^[28]	0.708 5	0.672 9	0.933 8	0.852 7	0.838 2	0.753 6
HCCF^[16]	0.722 6	0.680 9	0.932 7	0.847 4	0.854 6	0.765 2
EFBH^[32]	0.733 3	0.683 2	0.934 9	0.848 3	0.860 1	0.786 9
HC-CRKG^[8]	0.752 6	0.682 9	0.945 0	0.872 0	0.886 0	0.798 0
HTKR(本文)	0.759 4	0.694 2	0.957 9	0.895 4	0.862 8	0.794 7

方法	Book-Crossing		MovieLens-1M		Last.FM
方法	AUC	F₁	AUC	F₁	AUC	F₁
BPRFM^[5]	0.649 3	0.610 8	0.883 8	0.789 3	0.713 4	0.640 6
CKE^[21]	0.675 6	0.620 2	0.900 8	0.797 2	0.721 6	0.643 1
RippleNet^[25]	0.719 1	0.645 0	0.911 5	0.827 1	0.770 2	0.694 8
KGCN^[7]	0.682 1	0.629 3	0.907 8	0.831 0	0.757 9	0.701 9
KGIN^[26]	0.721 5	0.661 9	0.912 5	0.841 3	0.808 1	0.731 3
HGNN^[28]	0.708 5	0.672 9	0.933 8	0.852 7	0.838 2	0.753 6
HCCF^[16]	0.722 6	0.680 9	0.932 7	0.847 4	0.854 6	0.765 2
EFBH^[32]	0.733 3	0.683 2	0.934 9	0.848 3	0.860 1	0.786 9
HC-CRKG^[8]	0.752 6	0.682 9	0.945 0	0.872 0	0.886 0	0.798 0
HTKR(本文)	0.759 4	0.694 2	0.957 9	0.895 4	0.862 8	0.794 7

方法	Book-Crossing		MovieLens-1M		Last.FM
方法	AUC	F₁	AUC	F₁	AUC	F₁
HTKR	0.759 4	0.694 2	0.957 9	0.895 4	0.862 8	0.794 7
HTKR-全局超图	0.706 3	0.658 6	0.907 3	0.853 0	0.821 6	0.753 8
HTKR-非全局超图	0.718 6	0.678 2	0.916 0	0.875 3	0.844 5	0.776 5
HTKR-Transformer	0.718 8	0.669 7	0.922 7	0.889 1	0.829 5	0.779 6
HTKR-超图-Transformer	0.647 9	0.628 5	0.803 5	0.740 3	0.731 9	0.700 1
HTKR+10%噪声	0.760 7	0.702 2	0.959 8	0.899 1	0.867 9	0.805 4
HTKR+20%噪声	0.767 3	0.708 5	0.958 2	0.899 3	0.880 1	0.808 6
HTKR+50%噪声	0.624 7	0.640 8	0.914 0	0.811 9	0.754 6	0.701 2

基于超图表示学习和Transformer模型优化的知识感知推荐

Knowledge-aware recommendation based on hypergraph representation learning and Transformer model optimization

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