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
相关论文
共 37 条
  • [1] Liu X Z, Yu Y Y, Guo C, Et al., Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation, Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp. 121-130, (2014)
  • [2] Wu T, Liu Z W, Huang Q Q, Et al., Adversarial Robustness under Long-Tailed Distribution, Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8655-8664, (2021)
  • [3] Beel J, Gipp B, Langer S, Et al., Research-Paper Recommender Systems: A Literature Survey, International Journal on Digital Libraries, 17, 4, pp. 305-338, (2016)
  • [4] Zhang Y, Yang L B, Cai X Y, Et al., A Novel Personalized Citation Recommendation Approach Based on GAN, Proceedings of International Symposium on Methodologies for Intelligent Systems, pp. 268-278, (2018)
  • [5] Goyal P, Ferrara E., Graph Embedding Techniques, Applications, and Performance: A Survey, Knowledge-Based Systems, 151, pp. 78-94, (2018)
  • [6] Huang Lu, Lin Chuanjie, He Jun, Et al., Diversified Mobile App Recommendation Combining Topic Model and Collaborative Filtering, Journal of Software, 28, 3, pp. 708-720, (2017)
  • [7] Zhu Y F, Lin Q K, Lu H, Et al., Recommending Scientific Paper via Heterogeneous Knowledge Embedding Based Attentive Recurrent Neural Networks, Knowledge-Based Systems, 215, (2021)
  • [8] Dai Z H, Yang Z L, Yang Y M, Et al., Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000-6010, (2017)
  • [9] Vaswani A, Shazeer N, Parmar N, Et al., Attention is All You Need [C], Proceedings of the 31st Conference on Neural Information Processing Systems, (2017)
  • [10] Ji G L, He S Z, Xu L H, Et al., Knowledge Graph Embedding via Dynamic Mapping Matrix, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long papers), pp. 687-696, (2015)
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