| Autor: |
WANG Bingyuan, LIU Baisong, ZHANG Xueyuan, QIN Jiangcheng, DONG Qian, QIAN Jiangbo |
| Předmět: |
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| Zdroj: |
Journal of Computer Engineering & Applications; 6/1/2023, Vol. 59 Issue 11, p241-250, 10p |
| Abstrakt: |
With the expansion of academic information, scholars face a big challenge of efficiently selecting valid information in the era of big academic data. A venue recommendation system is one of the main ways to assist scholars in solving the information overload problem. This paper focuses on the issue of how to fit appropriate academic journals for manuscripts efficiently. It extracts diverse academic entities and edges from academic data for constructing academic heterogeneous information networks. This paper proposes a novel method for venue recommendation (SCVR) . Firstly, the topic information is extracted from the abstracts and topics by LDA and guides different types of nodes to map to the multi-topic feature space. Then, the meta-path contextual information is aggregated to the target node, forming a multi-topic node representation. Finally, the node representations from multiple mate-paths are combined into the final multi- topic node representations. SCVR learns the multi- topic node representations with paper content and network structure to venue recommendation.Experiments on two real academic datasets show that a heterogeneous information network recommendation incorporating article topics can effectively improve the performance of the venue recommendation. Compared with the current heterogeneous information network recommendation and traditional venue recommendation, the performance of SCVR has improved by an average of 2.7% and 19%, which indicates that SCVR has better performance in the area of venue recommendation. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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