基于异构图嵌入的论文个性化推荐算法

doi:10.11871/jfdc.issn.2096-742X.2023.06.14

数据与计算发展前沿 ›› 2023, Vol. 5 ›› Issue (6): 153-160.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.06.014

doi: 10.11871/jfdc.issn.2096-742X.2023.06.14

基于异构图嵌入的论文个性化推荐算法

赵成亮^1,²(),陈远平^1,^*()

1.中国科学院计算机网络信息中心，北京 100083
2.中国科学院大学，北京 100049

收稿日期:2022-06-06 出版日期:2023-12-20 发布日期:2023-12-25
通讯作者: *陈远平（E-mail: ypchen@cnic.cn）
作者简介:赵成亮，中国科学院计算机网络信息中心，中国科学院大学，硕士研究生，主要研究方向为数据挖掘、推荐技术。
本文承担工作为：模型设计，实验数据分析、论文写作。
ZHAO Chengliang is a master's student in the Computer Network Information Center of Chinese Academy of Sciences (University of Chinese Academy of Sciences). His main research interests are data mining and recommended technology.
In this paper, he undertakes the following tasks: model design, experimental data analysis, and paper writing.
E-mail: zhaochengliang@cnic.cn|陈远平，中国科学院计算机网络信息中心，高级工程师，主要研究方向为数据分析、决策分析模型研究、数据挖掘应用。
本文承担工作为：论文整体框架设计、研究指导。
CHEN Yuanping is a senior engineer from the Computer Network Information Center of Chinese Academy of Sciences. His main research interests are data analysis, decision analysis model research, and data mining applications.
In this paper, he undertakes the following tasks: the overall framework design and research guidance of the thesis.
E-mail: ypchen@cnic.cn
基金资助:
中国科学院“十四五”网络安全和信息化专项(CAS-WX2022GC-0301)

A Personalized Paper Recommendation Algorithm Based on Heterogeneous Graph Embedding

ZHAO Chengliang^1,²(),CHEN Yuanping^1,^*()

1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2022-06-06 Online:2023-12-20 Published:2023-12-25

摘要/Abstract

摘要：

【背景】 科技论文数量的快速增长使得如何快速查找或定位到感兴趣的文献资料成为了科研人员在科学研究过程中一个亟待解决的问题。【目的】 本文旨在研究并提出一种基于图嵌入的论文推荐算法，尝试解决面向用户的论文个性化推荐问题。【方法】 本文提出了一种基于异构图嵌入的论文个性化推荐算法。该算法通过异构图嵌入模型构建论文节点的嵌入表示，同时基于作者已发表的论文构建该作者的兴趣表示，最终利用两者之间的相似度对作者进行论文推荐。【结论】 在DBLP数据集上的实验证明了本文提出的模型及算法的有效性。

关键词: 图嵌入, 论文推荐, 异构图, 个性化推荐

Abstract:

[Background] With the rapid growth of the number of scientific papers, finding or locating the papers of interest has become an urgent problem for researchers in the process of scientific research. [Objective] This paper aims to study a paper recommendation algorithm to solve the problem of user-oriented personalized paper recommendations. [Methods] A personalized paper recommendation algorithm based on heterogeneous graph embedding is proposed, which learns the representation of the paper nodes, then the interest of the author is calculated according to the papers published by the author, and recommendations are then generated based on the similarity between author interests and the papers. [Results] Experiment on the DBLP dataset demonstrates the effectiveness of the model and the algorithm proposed in this paper.

Key words: graph embedding, paper recommendation, heterogeneous graph, personalized recommendation

赵成亮, 陈远平. 基于异构图嵌入的论文个性化推荐算法[J]. 数据与计算发展前沿, 2023, 5(6): 153-160.

ZHAO Chengliang, CHEN Yuanping. A Personalized Paper Recommendation Algorithm Based on Heterogeneous Graph Embedding[J]. Frontiers of Data and Computing, 2023, 5(6): 153-160, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.06.014.

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参考文献 20

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	Train-Small	Train-Big
论文节点	126，648	497，379
作者节点	34，622	196，004
Cite关系	268，516	2，250，742
Write关系	51，063	429，394
Co-work关系	135，236	962，246

参数名称	参数取值
numwalks	20
walklength	100
vecotr_size	128
window	5
sg	1
hs	1
epochs	10

	P@10	P@20	P@30	R@10	R@20	R@30	F1@10	F1@20	F1@30
DeepWalk	0.2213	0.1439	0.1068	0.1267	0.1565	0.1687	0.1546	0.1421	0.1236
node2vec	0.2707	0.1854	0.1407	0.1507	0.1939	0.2133	0.1860	0.1802	0.1608
BFS	0.2950	0.2139	0.1659	0.1646	0.2246	0.2511	0.2030	0.2087	0.1898
BFS-papp	0.3013	0.2184	0.1710	0.1693	0.2296	0.2596	0.2082	0.2133	0.1959
BFS-pp	0.3006	0.2165	0.1687	0.1674	0.2260	0.2546	0.2067	0.2107	0.1928
BFS-app	0.3143	0.2285	0.1766	0.1771	0.2397	0.2687	0.2177	0.2228	0.2026

	P@10	P@20	P@30	R@10	R@20	R@30	F1@10	F1@20	F1@30
DeepWalk	0.2593	0.1833	0.1461	0.1333	0.1807	0.2116	0.1667	0.1703	0.1615
node2vec	0.2941	0.2109	0.1680	0.1501	0.2069	0.2413	0.1883	0.1955	0.1850
BFS	0.3090	0.2276	0.1839	0.1545	0.2177	0.2567	0.1951	0.2082	0.2000
BFS-papp	0.3165	0.2310	0.1854	0.1604	0.2236	0.2625	0.2015	0.2123	0.2025
BFS-pp	0.3177	0.2366	0.1864	0.1643	0.2269	0.2674	0.2046	0.2073	0.2005
BFS-app	0.3384	0.2503	0.2011	0.1716	0.2421	0.2834	0.2155	0.2300	0.2192

方法	Small	Big
DeepWalk	2.39	3.48
node2vec	6.41	18.93
BFS	16.05	16.55
BFS-app	23.85	27.8

基于异构图嵌入的论文个性化推荐算法

A Personalized Paper Recommendation Algorithm Based on Heterogeneous Graph Embedding

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