Centrality-Based Paper Citation Recommender System

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

MuhammadAleem¹  AbdulSamad¹  MuhammadAzhar Iqbal¹  MuhammadArshad Islam² 

[1] Capital University of Science and Technology, Islamabad, Pakistan;FAST-National University of Computer and Emerging Sciences, Islamabad, Pakistan;

关键词: citation recommendation;    textual similarity;    topological similarity;   

DOI  :  10.4108/eai.13-6-2019.159121

来源: DOAJ

【 摘 要 】

Researchers cite papers in order to connect the new research ideas with previous research. For the purpose of finding suitable papers to cite, researchers spend a considerable amount of time and effort. To help researchers in finding relevant/important papers, we evaluated textual and topological similarity measures for citation recommendations. This work analyzes textual and topological similarity measures (i.e., Jaccard and Cosine) to evaluate which one performs well in finding similar papers? To find the importance of papers, we compute centrality measures (i.e., Betweeness, Closeness, Degree and PageRank). After evaluation, it is found that topological-based similarity via Cosine achieved 85.2% and using Jaccard obtained 61.9% whereas textualbased similarity via Cosine on abstract obtained 68.9% and using Cosine on title achieved 37.4% citation links. Likewise, textual-based similarity via Jaccard on abstract obtained 35.4% and using Jaccard on title achieved 28.3% citation links.

【 授权许可】

Unknown