Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding

被引：0
|
作者：
Li K. [1 ]
Niu Z. [1 ]
Shi K. [1 ,2 ]
Qiu P. [1 ]
机构：
[1] School of Computer Science & Technology, Beijing Institute of Technology, Beijing
[2] Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney
来源：
Data Analysis and Knowledge Discovery | 2023年 / 7卷 / 05期
关键词：
Academic Paper Knowledge Graph; Feature Fusion; Feature Learning; Knowledge Embedding; Paper Recommendation;
D O I：
10.11925/infotech.2096-3467.2022.0424
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
[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. © 2023 Data Analysis and Knowledge Discovery. All rights reserved.
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收藏
页码：48 / 59
页数：11
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