融合知识增强和多层抑噪的个性化推荐模型

谢珺; 王丹彤; 张博; 陈桂军; 吕佳琪; 雒雄艳

doi:10.11999/JEIT260051

融合知识增强和多层抑噪的个性化推荐模型

doi: 10.11999/JEIT260051 cstr: 32379.14.JEIT260051

太原理工大学，电子信息工程学院山西 030600

基金项目: 国家自然科学基金(62201377)，虚拟现实技术与系统国家重点实验室(北京航空航天大学)开放课题基金(No.VRLAB2022C11)，山西省回国留学项目(2024-61)

详细信息

作者简介:
谢珺：女，副教授，研究方向为智能信息处理、知识图谱理论和推荐系统

王丹彤：男，硕士研究生，研究方向为知识图谱、推荐系统

张博：男，副教授，研究方向为具身智能和智能信息处理

陈桂军：男，副教授，研究方向为智能信息处理和脑机接口

吕佳琪：女，硕士研究生，研究方向为知识图谱和人工智能

雒雄艳：女，硕士研究生，研究方向为大语言模型和推荐系统

通讯作者:
张博　zhangbo@tyut.edu.cn

中图分类号: TP391.3
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- 被引次数: 0
出版历程
- 收稿日期: 2026-01-14
- 修回日期: 2026-04-28
- 录用日期: 2026-05-12
- 网络出版日期: 2026-06-02

KE-HNS: Knowledge-Enhanced Personalized Recommender Model with Hierarchical Noise Suppression

School of Electronic and Information Engineering, Taiyuan University of Technology, Jinzhong 030600, China

Funds: NSFC (62201377), the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems (No.VRLAB2022C11), Shanxi Scholarship Council of China (2024-61)

摘要

摘要: 现有的基于知识图谱的推荐模型面临着噪声抑制不足、细粒度用户兴趣捕捉不充分以及信息利用不平衡等挑战，影响推荐的准确性。该文提出一种融合知识增强和多层抑噪的个性化推荐模型(Knowledge-Enhanced personalized recommender model with Hierarchical Noise Suppression, KE-HNS)。首先，KE-HNS提出了一种多层抑噪策略：在输入端构建降噪层，分别以二元掩码矩阵与掩盖低权重三元组的方式，剔除用户历史交互行为及知识图谱中的无关或误导性信息；在主干网络上设置抑噪层，利用实体关系空间隔离消除特征融合过程中的语义污染；同时构造对比学习压噪层，通过正负样本的比较压制无关实体噪声。其次，为更精准建模用户偏好与项目特性，利用图卷积网络、自适应权重层，借助降噪后的外部知识增强用户表征和项目表征。再次，通过构建用户-项目与项目-实体双视图对比学习机制，KE-HNS借助正负样本对自适应地平衡不同信息源的权重，从而缓解信息利用不平衡的问题。在三个公开基准数据集上的实验表明，与CG-KGR、KGIN和KG-DCRec等多种先进方法对比，该文提出的模型KE-HNS在AUC和F1指标上分别提升了0.94%–1.01%和0.43%–0.90%，在降噪性能对比和Recall@K等指标上也优于基准模型。
- 推荐系统 /
- 图神经网络 /
- 多层抑噪 /
- 知识增强 /
- 对比学习
Abstract: Objective In the big data and AI era, explosive information growth underpins the digital economy, yet filtering value from redundancy remains a key bottleneck. Personalized recommender systems are vital for precise matching and resource optimization. Integrating knowledge graphs enriches user–item representations, but current KG-based models suffer from weak noise suppression, coarse interest capture, and imbalanced information use, impairing performance. This paper proposes KE-HNS, a knowledge-enhanced recommender with a hierarchical multi-layer denoising strategy that fuses graph neural networks and contrastive learning. It systematically tackles noise, fine-grained preferences, and multi-source balance, markedly improving recommendation effectiveness. Methods KE-HNS introduces a multi-layer denoising paradigm. At input, an input denoising layer reduces noise via two sub-modules: user-item interaction denoising, which uses a learnable binary mask to drop noisy edges; and KG denoising enhancement, which scores triples by importance, identifies low-score ones, and masks them. The internal denoising layer preserves spatial independence by partitioning the entity-attribute space per relation, limiting high-order noise propagation. A compression denoising layer applies contrastive learning to further suppress noise and reinforce robust signals. To capture fine-grained interests, GCNs enhance user representations from interacted items and linked entities, while weight layers refine item representations using entity attributes and relations. For balanced information use, contrastive learning aligns user–item and item–entity views via positive/negative sampling, adaptively adjusting source weights. Matching is performed via inner product, producing a TOP-K recommendation list. Results and Discussions KE-HNS was assessed on three public datasets—Book-Crossing, MovieLens-1M, and Last.FM—via performance comparison, ablation studies, denoising evaluation, case analysis, and complexity assessment. For CTR prediction, it outperforms top baselines by 0.94%–1.01% in AUC and 0.43%–0.90% in F1 (Table 3). In Top-K recommendation, its Recall@K exceeds most state-of-the-art methods across nearly all K values, trailing CG-KGR only slightly on Last.FM (Fig. 7). Ablation results confirm that every sub-module contributes significantly to performance gains (Table 4). Denoising tests show the model filters noise effectively while preserving high prediction accuracy under noisy settings (Fig. 8). Complexity analysis indicates practical deployability in real-world scenarios (Table 5). Conclusions This paper presents KE-HNS, a personalized recommendation model that combines knowledge enhancement with a multi-layer suppression mechanism. While it delivers strong performance across multiple domains, it has notable limitations: the view contrast operation hampers computational efficiency; it relies heavily on the completeness of knowledge graph coverage; and current evaluations lack testing in emerging multimedia contexts. Experiments on benchmark datasets show that KE-HNS effectively aligns collaborative filtering signals with knowledge-aware semantics while mitigating noise, pointing to promising avenues for future work on computational optimization and dynamic knowledge integration.
- Recommender system /
- Graph convolutional network /
- Joint noise suppression /
- Knowledge enhancement /
- Contrastive learning

HTML全文

图 1 大数据时代下知识图谱赋能推荐系统

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图 2 KE-HNS模型框架示意图

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图 3 模块作用及多层降噪体现

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图 4 用户—项目交互降噪机制

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图 5 知识图降噪增强机制

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图 6 属性空间隔离建模过程

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图 7 Top-K推荐任务结果

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图 8 降噪性能对比

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图 9 KE-HNS模型在推荐系统领域有效性的例证

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表 1 数据集统计

数据集	知识图谱			用户项目交互
数据集	实体	关系	三元组	用户	项目	交互
MovieLens-1M	182011	12	1241996	6036	2445	753772
Last.FM	9366	60	15518	1872	3846	42346
Book-Crossing	77903	25	151500	17860	14976	139746

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表 2 基线模型简介

基线模型	时间	简介
CKE	2016	一种融合知识图谱与协同过滤的推荐系统模型，旨在整合多源异构信息。
RippleNet	2018	一种通过知识图谱语义关联将用户历史兴趣传播至候选项目的推荐系统模型。
KGAT	2019	一种采用协同知识图谱显式捕获交互与图谱间的高阶语义关系的推荐模型。
KGIN	2021	一种融合细粒度用户意图建模与关系路径感知聚合的推荐系统模型。
CG-KGR	2022	一种通过协同引导机制融合用户-项目交互数据与外部知识图谱的推荐模型。
MDCLBR	2024	一种基于用户-项目簇、用户-项目及项目簇-项目三视角多视图对比学习模型。
KG-DCRec	2025	一种融合差异化去噪与双对比学习的图神经网络推荐模型。

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表 3 AUC和F1结果对比

模型	MovieLens-1M		Last.FM		Book-Crossing
模型	AUC	F1	AUC	F1	AUC	F1
CKE	0.9066	0.8032	0.7471	0.6743	0.6762	0.6235
RippleNet	0.9188	0.8420	0.7762	0.7025	0.7217	0.6465
KGAT	0.9110	0.8426	0.8292	0.7424	0.7314	0.6547
CG-KGR	0.9114	0.8422	0.8493	0.7527	0.7419	0.6540
KGIN	0.9190	0.8440	0.8486	0.7602	0.7265	0.6619
MDCLBR	0.9213	0.8438	0.8502	0.7593	0.7506	0.6642
KG-DCRec	0.9261	0.8547	0.8477	0.7706	0.7541	0.6674
KE-HNS	0.9349	0.8624	0.8588	0.7739	0.7612	0.6731
IMPROVE(%)	0.95	0.90	1.01	0.43	0.94	0.85

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表 4 消融实验结果

变体模型	AUC	F1
w/o INR&INS&CNS	0.9005	0.8268
w/o INR&INS	0.9058	0.8302
w/o INR&CNS	0.9040	0.8317
w/o INS&CNS	0.9192	0.8465
w/o INR	0.9208	0.8483
w/o CNS	0.9293	0.8598
w/o INS	0.9301	0.8613
KE-HNS	0.9349	0.8624

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表 5 算法复杂度和训练时间对比

模型	计算复杂度	训练时间/s
MDCLBR	O(L(2\|V\|Ed+E\|d)+\|V\|²log\|V\| +2\|V\|²d+k\|V\|d+\|N\|d)	5.404
CG-KGR	O(L(\|V\|d²+2\|N\|d)+\|V\|d²+\|N\|d)	2.2147
KGIN	O(d^L+\|V\|d²+2\|E\|d +\|V\|d+\|N\|d)	506.8
KE-HNS	O(L(kd²+\|U\|\|\|I\|)²d+2\|E\|kd) +\|E\|Md+2\|V\|²d)	7.548

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