Literature Recommendation Algorithm Integrating High-Order Similarity of Motif Structure

doi:10.11925/infotech.2096-3467.2022.0715

Data Analysis and Knowledge Discovery

2023, Vol. 7

Issue (7): 146-155 DOI: 10.11925/infotech.2096-3467.2022.0715

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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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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 words： Literature Recommendation Motif Structure User High-Order Similarity Matrix Factorization

Received: 11 July 2022 Published: 21 March 2023

ZTFLH:	TP391
	G250

Fund:Academic Training Project for UIR Students(3262021SYJ007)

Corresponding Authors: Guo Yuhong，ORCID：0000-0003-3336-0611，E-mail： yhguo@uir.cn。

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Cite this article:

Chen Liu, Guo Yuhong. Literature Recommendation Algorithm Integrating High-Order Similarity of Motif Structure. Data Analysis and Knowledge Discovery, 2023, 7(7): 146-155.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0715 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I7/146

Four Node Network Example

Seven Three-Node Motif Structures

Calculation of Adjacency Matrix Based on M₄Structure

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

Symbol Definition

Algorithm Flow

Example of Motif Structure

User-Literature Collection Relationship and Literature-Literature Citation Relationship

Calculation Example of High-Order Similarity Matrix Based on Motif Structure

Default Value of Experimental Parameters

Performance Comparison of Different Algorithms

Changes of RMSE Under Different Fusion Parameters σ

Changes of MAE Under Different Fusion Parameters σ

Results of Different Equilibrium Parameters α

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