Name
Abstract | ||
|---|---|---|
One of the tasks of scientific paper writing is to recommend. When the number of references is increased, there is no clear classification and the similarity measure of the recommendation system will show poor performance. In this work, we propose a novel recommendation research approach using classification, clustering and recommendation models integrated into the system. In an evaluation on ACL Anthology papers network data, we effectively use complex network of knowledge tree node degrees (refer to the number of papers) to enhance the accuracy of recommendation. The experimental results show that our model generates better recommended citation, achieving 10% higher accuracy and 8% higher F-score than to the keyword march method when the data is big enough. We make full use of the collective intelligence to serve the public. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1504/IJCSE.2019.101883 | INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING |
Keywords | Field | DocType |
citation recommendation, classification, clustering, similarity, citation network | Data science,Collective intelligence,Computer science,Citation,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 4 | 1742-7185 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zhao | 1 | 1 | 1.04 |
| Zhao Li | 2 | 2 | 1.73 |
| Bitao Li | 3 | 0 | 0.34 |
| Ke-Qing He | 4 | 428 | 63.80 |
| Junfei Guo | 5 | 0 | 0.34 |