Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding
- Publication Type:
- Journal Article
- Citation:
- Data Analysis and Knowledge Discovery, 2023, 7, (5), pp. 48-59
- Issue Date:
- 2023-05-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding.pdf | Published version | 1.11 MB |
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[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.
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