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数据分析与知识发现  2023, Vol. 7 Issue (7): 146-155     https://doi.org/10.11925/infotech.2096-3467.2022.0715
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
融合Motif结构高阶相似度的文献推荐算法*
陈柳,郭宇红()
国际关系学院网络空间安全学院 北京 100091
Literature Recommendation Algorithm Integrating High-Order Similarity of Motif Structure
Chen Liu,Guo Yuhong()
School of Cyber Science and Engineering, University of International Relations, Beijing 100091, China
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摘要 

【目的】 将协同过滤方法应用到文献推荐领域,融入用户余弦相似度网络中Motif结构反映出的高阶相似特征,提高推荐的质量。【方法】 通过用户收藏文献的行为信息和文献间的引用关系构建用户对文献的偏好数据;在基于用户-文献收藏行为信息的用户余弦相似度网络中,利用网络中的子图——Motif结构捕获高阶相似度;最后将用户余弦相似度和基于Motif结构的高阶相似度融入矩阵分解推荐算法中,预测用户对文献的偏好。【结果】 相较于传统的矩阵分解推荐算法,本文算法在RMSE和MAE指标上分别降低0.0482和0.0379。【局限】 未考虑文献的时间衰减性。【结论】 本文算法降低了用户偏好预测误差,提高了推荐质量。

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陈柳
郭宇红
关键词 文献推荐Motif结构用户高阶相似度矩阵分解    
Abstract

[Objective] This paper applies the collaborative filtering method to the field of literature recommendation. It incorporates high-order similarity features reflected by the Motif structure in the user cosine similarity network to improve the recommendation quality. [Methods] Firstly, we constructed the user preference data for literature using their behavior information of collecting literature and the citation relationship between literature. Secondly, in the user cosine similarity network based on user literature collection behavior information, we captured the high-order similarity with subgraph—Motif structure within the network. Finally, we integrated user cosine and high-order similarity based on Motif structure into the matrix factorization recommendation algorithm to predict user preferences for literature. [Results] Compared with the traditional matrix factorization recommendation algorithms, this algorithm's RMSE and MAE metrics were reduced by 0.0482 and 0.0379, respectively. [Limitations] The proposed algorithm does not consider the temporal decay of the literature. [Conclusions] The new algorithm reduces the prediction error of user preferences and improves the literature recommendation quality.

Key wordsLiterature Recommendation    Motif Structure    User High-Order Similarity    Matrix Factorization
收稿日期: 2022-07-11      出版日期: 2023-03-21
ZTFLH:  TP391  
  G250  
基金资助:*国际关系学院大学生学术支持计划项目的研究成果之一(3262021SYJ007)
通讯作者: 郭宇红,ORCID:0000-0003-3336-0611,E-mail: yhguo@uir.cn。   
引用本文:   
陈柳, 郭宇红. 融合Motif结构高阶相似度的文献推荐算法*[J]. 数据分析与知识发现, 2023, 7(7): 146-155.
Chen Liu, Guo Yuhong. Literature Recommendation Algorithm Integrating High-Order Similarity of Motif Structure. Data Analysis and Knowledge Discovery, 2023, 7(7): 146-155.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.0715      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2023/V7/I7/146
Fig.1  四节点网络示例
Fig.2  7种三节点Motif结构
Fig.3  基于M4结构的邻接矩阵计算
符号 描述
P m × n 用户-文献收藏矩阵
Q n × n 文献-文献引用矩阵
F 用户集合
L 文献集合
C m × n 用户-文献初始偏好矩阵
R m × n 用户-文献偏好矩阵
i , k 用户集中的某位用户
j 文献集中的某篇文献
T m × m 用户余弦相似度矩阵
H m × m 基于矩阵 T且对角线上值为0的矩阵
G 基于邻接矩阵H的加权有向图
A 基于图G的无权邻接矩阵
D m × m 基于Motif结构的用户高阶相似度矩阵
S m × m 用户增强相似度矩阵
R ^ m × n 用户-文献预测偏好矩阵
U m × d 用户特征矩阵
V n × d 文献特征矩阵
Table 1  符号定义
Fig.4  算法流程
Fig.5  Motif结构示例
Fig.6  用户-文献收藏关系和文献-文献引用关系
Fig.7  基于Motif结构高阶相似度矩阵的计算示例
参数名称 默认值 参数名称 默认值
梯度下降步长 0.01 正则项参数β 0.001
迭代次数 300 融合参数σ 11
特征维度 20 平衡参数α 10
Table 2  实验参数默认值
算法 RMSE MAE
BiasSVD 0.350 6 0.233 1
MF 0.346 3 0.231 9
SVD++ 0.347 7 0.231 7
JMF-UCP 0.345 7 0.224 4
本文算法 0.298 1 0.194 0
Table 3  不同算法性能对比
Fig.8  不同融合参数σ下RMSE的变化
Fig.9  不同融合参数σ下MAE的变化
平衡参数α RMSE MAE
0 0.304 8 0.194 3
10 0.298 1 0.194 0
20 0.300 1 0.196 4
Table 4  不同平衡参数α的实验结果
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