Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception Article Swipe
Yuyao Zeng
,
Junping Du
,
Zhe Xue
,
Ang Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.03295
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.03295
Existing research usually utilizes side information such as social network or item attributes to improve the performance of collaborative filtering-based recommender systems. In this paper, the knowledge graph with user perception is used to acquire the source of side information. We proposed KGUPN to address the limitations of existing embedding-based and path-based knowledge graph-aware recommendation methods, an end-to-end framework that integrates knowledge graph and user awareness into scientific and technological news recommendation systems. KGUPN contains three main layers, which are the propagation representation layer, the contextual information layer and collaborative relation layer. The propagation representation layer improves the representation of an entity by recursively propagating embeddings from its neighbors (which can be users, news, or relationships) in the knowledge graph. The contextual information layer improves the representation of entities by encoding the behavioral information of entities appearing in the news. The collaborative relation layer complements the relationship between entities in the news knowledge graph. Experimental results on real-world datasets show that KGUPN significantly outperforms state-of-the-art baselines in scientific and technological news recommendation.
Related Topics
Concepts
Computer science
Recommender system
Graph
Information retrieval
Representation (politics)
Collaborative filtering
Layer (electronics)
Perception
Feature learning
Embedding
World Wide Web
Theoretical computer science
Artificial intelligence
Biology
Chemistry
Neuroscience
Law
Organic chemistry
Political science
Politics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.03295
- https://arxiv.org/pdf/2210.03295
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4304192662
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4304192662Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2210.03295Digital Object Identifier
- Title
-
Scientific and Technological News Recommendation Based on Knowledge Graph with User PerceptionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-07Full publication date if available
- Authors
-
Yuyao Zeng, Junping Du, Zhe Xue, Ang LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2210.03295Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2210.03295Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2210.03295Direct OA link when available
- Concepts
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Computer science, Recommender system, Graph, Information retrieval, Representation (politics), Collaborative filtering, Layer (electronics), Perception, Feature learning, Embedding, World Wide Web, Theoretical computer science, Artificial intelligence, Biology, Chemistry, Neuroscience, Law, Organic chemistry, Political science, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.path-based | 51 |
| abstract_inverted_index.perception | 30 |
| abstract_inverted_index.real-world | 157 |
| abstract_inverted_index.scientific | 67, 167 |
| abstract_inverted_index.complements | 144 |
| abstract_inverted_index.graph-aware | 53 |
| abstract_inverted_index.information | 5, 86, 122, 133 |
| abstract_inverted_index.limitations | 46 |
| abstract_inverted_index.outperforms | 163 |
| abstract_inverted_index.performance | 16 |
| abstract_inverted_index.propagating | 104 |
| abstract_inverted_index.propagation | 81, 93 |
| abstract_inverted_index.recommender | 20 |
| abstract_inverted_index.recursively | 103 |
| abstract_inverted_index.Experimental | 154 |
| abstract_inverted_index.information. | 39 |
| abstract_inverted_index.relationship | 146 |
| abstract_inverted_index.collaborative | 18, 89, 141 |
| abstract_inverted_index.significantly | 162 |
| abstract_inverted_index.technological | 69, 169 |
| abstract_inverted_index.recommendation | 54, 71 |
| abstract_inverted_index.relationships) | 115 |
| abstract_inverted_index.representation | 82, 94, 98, 126 |
| abstract_inverted_index.embedding-based | 49 |
| abstract_inverted_index.filtering-based | 19 |
| abstract_inverted_index.recommendation. | 171 |
| abstract_inverted_index.state-of-the-art | 164 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |