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

XIE Jun; WANG Dantong; ZHANG Bo; CHEN Guijun; LV Jiaqi; LUO Xiongyan

doi:10.11999/JEIT260051

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XIE Jun, WANG Dantong, ZHANG Bo, CHEN Guijun, LV Jiaqi, LUO Xiongyan. KE-HNS: Knowledge-Enhanced Personalized Recommendation Model with Hierarchical Noise Suppression[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260051

Citation:

XIE Jun, WANG Dantong, ZHANG Bo, CHEN Guijun, LV Jiaqi, LUO Xiongyan. KE-HNS: Knowledge-Enhanced Personalized Recommendation Model with Hierarchical Noise Suppression[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260051

Citation:

XIE Jun, WANG Dantong, ZHANG Bo, CHEN Guijun, LV Jiaqi, LUO Xiongyan. KE-HNS: Knowledge-Enhanced Personalized Recommendation Model with Hierarchical Noise Suppression[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260051

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

doi: 10.11999/JEIT260051 cstr: 32379.14.JEIT260051

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

Funds: The National Natural Science Foundation of China(62201377), The Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems (VRLAB2022C11), Shanxi Scholarship Council of China (2024-61)

Received Date: 2026-01-14
Accepted Date: 2026-05-12
Rev Recd Date: 2026-04-28

Available Online: 2026-06-02

Abstract

Abstract

Objective In the era of Big Data and Artificial Intelligence (AI), rapid information growth has increased the difficulty of filtering valuable content from redundant data. Personalized recommender systems are key tools for accurate information matching and resource allocation. Knowledge Graphs (KGs) can enrich user-item representations. However, current KG-based recommendation models still face weak noise suppression, coarse-grained user-interest modeling, and imbalanced use of heterogeneous information, which reduce recommendation accuracy. This paper proposes Knowledge-Enhancedpersonalized recommender model with HierarchicalNoise Suppression (KE-HNS), which integrates knowledge enhancement with hierarchical noise suppression. By combining graph representation learning and contrastive learning, KE-HNS addresses noise interference, fine-grained preference modeling, and multi-source information balance, thereby improving recommendation performance. Methods KE-HNS adopts a hierarchical noise-suppression paradigm. At the input stage, Input Noise Reduction (INR) is used to reduce noise from two sources. For user-item interactions, a learnable binary mask matrix is used to remove noisy edges. For KG denoising enhancement, triples are scored by importance, low-score triples are identified with a Bottom-K strategy, and noisy triples are masked. At the feature-fusion stage, Isolated Noise Suppression (INS) is used to preserve spatial independence by partitioning entity-attribute spaces according to relation type. This design limits high-order noise propagation and semantic contamination. At the representation-optimization stage, Comparative Noise Suppression (CNS) is implemented through contrastive learning to suppress irrelevant entity noise and strengthen robust semantic signals. To capture fine-grained user interests, Graph Convolutional Networks (GCNs) are used to enhance user representations from historical interactions and related entities. Adaptive weight layers further refine item representations by using entity attributes and relations. To balance heterogeneous information, a dual-view contrastive learning mechanism is constructed between the user-item view and the item-entity view. Positive and negative sample pairs are used to adaptively adjust the weights of different information sources. Finally, user and item representations are matched by inner product to generate the Top-K recommendation list. Results and Discussions KE-HNS is evaluated on three public datasets, Book-Crossing, MovieLens-1M, and Last.FM, through performance comparison, ablation experiments, denoising evaluation, case analysis, and complexity assessment. For Click-Through Rate (CTR) prediction, KE-HNS outperforms the best baseline models by 0.94%～1.01% in Area Under the Curve (AUC) and 0.43%～0.90% in F1-score (Table 3). For Top-K recommendation, its Recall@K is higher than those of most advanced methods across nearly all K values, with only a slight gap behind CG-KGR on Last.FM (Fig. 7). The ablation results show that all three denoising components contribute to the performance gains (Table 4). The denoising evaluation shows that KE-HNS effectively suppresses noise and maintains high prediction accuracy under noisy conditions (Fig. 8). The complexity analysis further indicates that the model remains feasible for practical deployment (Table 5). Conclusions This paper presents KE-HNS, a personalized recommendation model that combines knowledge enhancement with hierarchical noise suppression. By reducing noise interference and balancing collaborative filtering signals with knowledge-aware semantics, KE-HNS improves recommendation accuracy across multiple benchmark datasets. The model still has limitations in computational efficiency and depends on the coverage and completeness of the KG. Future work may focus on computational optimization and dynamic knowledge integration.
- Recommender system,
- Graph neural network,
- Hierarchical noise suppression,
- Knowledge enhancement,
- Contrastive learning

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References(33)

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