Personalized literature recommendation based on heterogeneous entity academic network
Although researchers have benefited from big scholarly data, it is still very difficult for them to quickly and accurately find the suitable literature in the massive literature. In recent years, the research on personalized literature recommendation with the help of academic data has gradually attr...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823002033 |
| _version_ | 1797663861396996096 |
|---|---|
| author | Hongwu Qin Xianzhe Han Xiuqin Ma Wenying Yan |
| author_facet | Hongwu Qin Xianzhe Han Xiuqin Ma Wenying Yan |
| author_sort | Hongwu Qin |
| collection | DOAJ |
| description | Although researchers have benefited from big scholarly data, it is still very difficult for them to quickly and accurately find the suitable literature in the massive literature. In recent years, the research on personalized literature recommendation with the help of academic data has gradually attracted the attention of scholars. However, the existing works are mainly based on the similarity of literature content, and ignore the important information of scholars such as research fields and affiliations, which leads to insufficient personalization of recommendation results and there is still room for improvement in accuracy. In this paper, a new personalized literature recommendation method named PR-HeAN is proposed. This method also considers the similarity of literature, and more importantly, introduces a heterogeneous entity academic network. It combines the literature similarity obtained from a K-order literature co-citation network with the literature recommendation probability obtained from the heterogeneous entity academic network to generate the recommendation results. The heterogeneous network is generated from the K-order literature co-citation network, which integrates five types of academic entities, including literature, scholars, research fields, affiliations and publication venues, and enriches the representation information of the literature. The experimental results on two datasets show that the proposed method outperforms four baseline algorithms in terms of recall, accuracy and F1 value. |
| first_indexed | 2024-03-11T19:20:54Z |
| format | Article |
| id | doaj.art-45bd3deea1c646f18cea6c7d0ba32ef8 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-11T19:20:54Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-45bd3deea1c646f18cea6c7d0ba32ef82023-10-07T04:33:57ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-09-01358101649Personalized literature recommendation based on heterogeneous entity academic networkHongwu Qin0Xianzhe Han1Xiuqin Ma2Wenying Yan3College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaCorresponding author.; College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaAlthough researchers have benefited from big scholarly data, it is still very difficult for them to quickly and accurately find the suitable literature in the massive literature. In recent years, the research on personalized literature recommendation with the help of academic data has gradually attracted the attention of scholars. However, the existing works are mainly based on the similarity of literature content, and ignore the important information of scholars such as research fields and affiliations, which leads to insufficient personalization of recommendation results and there is still room for improvement in accuracy. In this paper, a new personalized literature recommendation method named PR-HeAN is proposed. This method also considers the similarity of literature, and more importantly, introduces a heterogeneous entity academic network. It combines the literature similarity obtained from a K-order literature co-citation network with the literature recommendation probability obtained from the heterogeneous entity academic network to generate the recommendation results. The heterogeneous network is generated from the K-order literature co-citation network, which integrates five types of academic entities, including literature, scholars, research fields, affiliations and publication venues, and enriches the representation information of the literature. The experimental results on two datasets show that the proposed method outperforms four baseline algorithms in terms of recall, accuracy and F1 value.http://www.sciencedirect.com/science/article/pii/S1319157823002033Personalized literature recommendationHeterogeneous entitiesAcademic networkGraph Convolutional Network |
| spellingShingle | Hongwu Qin Xianzhe Han Xiuqin Ma Wenying Yan Personalized literature recommendation based on heterogeneous entity academic network Journal of King Saud University: Computer and Information Sciences Personalized literature recommendation Heterogeneous entities Academic network Graph Convolutional Network |
| title | Personalized literature recommendation based on heterogeneous entity academic network |
| title_full | Personalized literature recommendation based on heterogeneous entity academic network |
| title_fullStr | Personalized literature recommendation based on heterogeneous entity academic network |
| title_full_unstemmed | Personalized literature recommendation based on heterogeneous entity academic network |
| title_short | Personalized literature recommendation based on heterogeneous entity academic network |
| title_sort | personalized literature recommendation based on heterogeneous entity academic network |
| topic | Personalized literature recommendation Heterogeneous entities Academic network Graph Convolutional Network |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823002033 |
| work_keys_str_mv | AT hongwuqin personalizedliteraturerecommendationbasedonheterogeneousentityacademicnetwork AT xianzhehan personalizedliteraturerecommendationbasedonheterogeneousentityacademicnetwork AT xiuqinma personalizedliteraturerecommendationbasedonheterogeneousentityacademicnetwork AT wenyingyan personalizedliteraturerecommendationbasedonheterogeneousentityacademicnetwork |