Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding
Li Kaijun1,Niu Zhendong1(),Shi Kaize1,2,Qiu Ping1
1School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China 2Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney 2007, Australia
[Objective] This paper proposes a new model that integrates multiple features to provide accurate paper recommendation services for researchers. [Methods] First, we designed a feature extraction framework to extract and fuse entity relation features and topic features from the knowledge graph and the content of academic papers, respectively. Then, we proposed a paper recommendation method based on the knowledge embedding-based encoding-decoding model, which improved the learning effect of high-dimensional fusion features. [Results] We examined our new model on the DBLP-v11 dataset. The proposed method improved the Recall and MRR scores by 8.9% and 2.9%, respectively, compared with the suboptimal model. [Limitations] The proposed graph feature learning method does not consider the weight of entities in the real environment. [Conclusions] The new paper recommendation method could effectively learn high-dimensional features, which provide guidance for subsequent research.
李锴君, 牛振东, 时恺泽, 邱萍. 基于学术知识图谱及主题特征嵌入的论文推荐方法*[J]. 数据分析与知识发现, 2023, 7(5): 48-59.
Li Kaijun, Niu Zhendong, Shi Kaize, Qiu Ping. Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding. Data Analysis and Knowledge Discovery, 2023, 7(5): 48-59.
Remote Policy Enforcement ···Execution in Mobile Environments
作者信息
作者ID、姓名以及隶属工作机构
出版机构
出版机构 ID 以及名称
年份
2013
参考论文
本篇论文引用的论文 ID 组成的列表
摘要
Both in ···viableand effective.
Table 3 数据集元数据
Top N
KE-EDM （本文）
HKE-ARNN
Citeomatic
ClusCite
VOPRec
查准率
查全率
F1 分数
查准率
查全率
F1 分数
查准率
查全率
F1 分数
查准率
查全率
F1 分数
查准率
查全率
F1 分数
1
0.686
0.337
0.452
0.654
0.062
0.113
0.469
0.039
0.072
0.412
0.019
0.036
0.313
0.053
0.091
5
0.516
0.406
0.454
0.511
0.162
0.246
0.348
0.140
0.200
0.352
0.152
0.212
0.187
0.160
0.172
10
0.265
0.463
0.337
0.281
0.291
0.286
0.294
0.235
0.261
0.242
0.212
0.226
0.159
0.280
0.203
20
0.150
0.682
0.247
0.160
0.593
0.252
0.222
0.354
0.273
0.195
0.300
0.236
0.135
0.410
0.203
50
0.064
0.705
0.117
0.067
0.596
0.120
0.139
0.539
0.221
0.111
0.427
0.176
0.059
0.460
0.105
Table 4 KE-EDM模型与基线模型的性能
Fig.2 各模型的MRR分数
主题特征
Precision@20
Recall@20
F1分数
MRR
标题&摘要
0.151
0.682
0.247
0.687
标题
0.150
0.655
0.244
0.659
摘要
0.151
0.662
0.245
0.686
Table 5 不同主题特征的嵌入对模型性能的影响分析
Fig.3 不同维度的向量特征对模型推荐性能的影响
方法
Precision@20
Recall@20
F1分数
MRR
TransD
0.151
0.682
0.247
0.687
TransR
0.150
0.675
0.245
0.689
TransH
0.146
0.664
0.239
0.656
TransE
0.144
0.650
0.235
0.649
Table 6 不同图谱特征表示学习方法对模型性能的影响分析
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