融合混合社区与簇级特征的KAN-CTR预测模型

钱忠胜; 饶雨贤; 吴敏璇; 彭劭强; 王蓉蓉; 许克文

doi:10.12263/DZXB.20250993

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融合混合社区与簇级特征的KAN-CTR预测模型

学术论文 | 更新时间：2026-06-04

- 融合混合社区与簇级特征的KAN-CTR预测模型
- A KAN-CTR Prediction Model Integrating Hybrid Community and Cluster-Level Feature
- 电子学报 2026年54卷第1期页码：395-416
- 作者机构：
  
  江西财经大学计算机与人工智能学院，江西南昌 330013
- 作者简介：
  
  [ "钱忠胜男，1977年1月出生于江西省鹰潭市。2008年在上海大学获工学博士学位。江西财经大学教授，博士生导师。主要研究方向为推荐系统、人工智能、软件工程等。E-mail: changesme@163.com" ]
  [ "饶雨贤女，2000年2月出生于江西省丰城市。江西财经大学计算机与人工智能学院硕士研究生。主要研究方向为推荐系统、软件工程等。E-mail: ryxxx2014@163.com" ]
  [ "吴敏璇女，2001年7月出生于江西省景德镇市。江西财经大学计算机与人工智能学院硕士研究生。主要研究方向为推荐系统、软件工程等。E-mail: 1638457774@ qq.com" ]
  [ "彭劭强男，2001年7月出生于江西省景德镇市。江西财经大学计算机与人工智能学院硕士研究生。主要研究方向为推荐系统、软件工程等。E-mail: 2376389837@qq.com" ]
  [ "王蓉蓉女，2003年4月出生于江西省萍乡市。江西财经大学计算机与人工智能学院硕士研究生。主要研究方向为推荐系统、软件工程等。E-mail: 1804193055@qq.com" ]
  [ "许克文男，2001年2月出生于江西省上饶市。江西财经大学计算机与人工智能学院硕士研究生。主要研究方向为推荐系统、软件工程等。E-mail: 2576140701@qq.com" ]
- 基金信息：
  
  国家自然科学基金(62262025);赣鄱俊才支持计划-主要学科学术和技术带头人培养项目-领军人才（学术类）(20243BCE51024)
- DOI：10.12263/DZXB.20250993
  中图分类号： TP399;
- 收稿：2026-01-10，
  
  录用：2026-01-23，
  
  纸质出版：2026-01-25
- 稿件说明：
移动端阅览
钱忠胜, 饶雨贤, 吴敏璇, 等. 融合混合社区与簇级特征的KAN-CTR预测模型[J]. 电子学报, 2026, 54(01): 395-416.

QIAN Zhongsheng, RAO Yuxian, WU Minxuan, et al. A KAN-CTR Prediction Model Integrating Hybrid Community and Cluster-Level Feature[J]. Acta Electronica Sinica, 2026, 54(01): 395-416.
钱忠胜, 饶雨贤, 吴敏璇, 等. 融合混合社区与簇级特征的KAN-CTR预测模型[J]. 电子学报, 2026, 54(01): 395-416. DOI：10.12263/DZXB.20250993

QIAN Zhongsheng, RAO Yuxian, WU Minxuan, et al. A KAN-CTR Prediction Model Integrating Hybrid Community and Cluster-Level Feature[J]. Acta Electronica Sinica, 2026, 54(01): 395-416. DOI：10.12263/DZXB.20250993

摘要

点击率（Click-Through Rate， CTR）预测是推荐系统的核心任务，其目标是通过用户历史行为与项目特征建模，预测用户对候选项目产生点击行为的概率。然而，现有CTR方法在建模全局交互结构、提取多跳邻居信息及提升高维特征交互学习效率等方面仍存在问题。用户与项目之间的交互通常呈现多层次、强结构化的关联特征，若直接建模则计算量过大且难以捕捉不同层级邻域之间的语义关系，从而限制对潜在语义关联与用户偏好的深入挖掘，而且现有CTR模型多依赖传统神经网络的固定激活函数，在建模高阶非线性特征交互时灵活性不足，易出现特征冗余、泛化能力弱等问题，导致预测精度难以进一步提升。针对这些问题，提出一种融合混合社区划分与簇级特征提取的Kolmogorov-Arnold Networks（KAN）架构CTR预测模型（A KAN-CTR prediction model integrating Hybrid Community and Cluster-level Feature， HCCF-KCTR）。首先，设计一种层次化的混合社区聚类策略，结合粗粒度的全局社区划分与细粒度的簇内优化，将复杂的全局交互关系解构为结构清晰、语义连贯的簇级单元，在保留关键结构信息的同时显著降低建模复杂度。其次，基于全局社区划分结果对多跳邻居进行簇级映射，并引入簇感知注意力池化机制（Attention Pooling），动态评估每跳邻居在簇内及簇间的语义重要性，自适应分配注意力权重，生成高质量的多跳邻居簇级嵌入表示，以充分捕捉用户与项目间的多层次交互特征。最后，利用KAN网络可学习函数替代固定激活函数，构建多个跨跳跨簇特征组合，将复杂的多跳交互特征转化为可解释的低阶函数组合表达，实现结构信息与语义特征的深度融合，进一步提升模型的预测精度与表达能力。在MovieLens、Electronics、Book及Taobao四个真实数据集上与13种主流CTR模型进行对比，实验结果表明，在AUC、GAUC和LogLoss这3个指标上，所提模型HCCF-KCTR相对已有最优基线模型分别平均最少提升2.74%、2.19%、3.68%，验证了其在特征交互建模与预测方面的优越性。此外，本文还通过消融实验、参数敏感性实验以及模型效率实验，验证了各模块的必要性、协同有效性，以及模型在整体效率上的均衡性，进一步阐明了模型具有良好的泛化能力。

Abstract

click-through rate (CTR) prediction is a core task in recommendation systems

whose goal is to predict the probability that a user will click on a candidate item by modeling the user’s historical behaviors and item features. However

existing CTR methods still have problems in modeling global interaction structures

extracting multi-hop neighbor information

and improving the efficiency of high-dimensional feature interaction learning. The interactions between users and items usually exhibit multi-level and strongly structured association characteristics; direct modeling will lead to excessive computational complexity and difficulty in capturing the semantic relationships between different levels of neighborhoods

thereby limiting the in-depth exploration of potential semantic associations and user preferences. Moreover

most existing CTR models rely on fixed activation functions of traditional neural networks

which lack flexibility in modeling high-order nonlinear feature interactions

and are prone to problems such as feature redundancy and weak generalization ability

resulting in difficulty in further improving prediction accuracy. To address these problems

this paper proposes a kolmogorov-arnold networks (KAN)-based CTR prediction model integrating hybrid community division and cluster-level feature extraction (HCCF-KCTR). Firstly

a hierarchical hybrid community clustering strategy is designed

which combines coarse-grained global community division and fine-grained intra-cluster optimization to decompose complex global interaction relationships into cluster-level units with clear structure and coherent semantics. This strategy significantly reduces the modeling complexity while retaining key structural information. Secondly

based on the results of global community division

multi-hop neighbors are mapped at the cluster level

and a cluster-aware attention pooling mechanism is introduced to dynamically evaluate the semantic importance of each hop of neighbors within and between clusters

adaptively assign attention weights

and generate high-quality cluster-level embedding representations of multi-hop neighbors

so as to fully capture the multi-level interaction characteristics between users and items. Finally

the learnable function of the KAN network is used to replace the fixed activation function

and multiple cross-hop and cross-cluster feature combinations are constructed to convert complex multi-hop interaction features into interpretable low-order function combination

realizing the in-depth fusion of structural information and semantic features

and further improving the prediction accuracy and expressive ability of the model. Comparative experiments are conducted with 13 mainstream CTR models on four real-world datasets

namely MovieLens

Electronics

Book

and Taobao. The experimental results show that

in terms of the three metrics of AUC

GAUC

and LogLoss

the proposed HCCF-KCTR model achieves an average minimum improvement of 2.74%

2.19%

and 3.68% respectively compared with the existing optimal baseline model

verifying its superiority in feature interaction modeling and prediction. In addition

this work verifies the necessity and synergistic effectiveness of each module as well as the balance of the model in overall efficiency through ablation experiments

parameter sensitivity experiments and model efficiency experiments

further demonstrating that the model has excellent generalization ability.

关键词

Keywords

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