Name
Title | ||
|---|---|---|
Modeling of cross-disciplinary collaboration for potential field discovery and recommendation based on scholarly big data. |
Abstract | ||
|---|---|---|
The promise of cross-disciplinary scientific collaboration has recently been proven by both technological innovation and scientific research. Much effort has been spent on research collaboration recommendation. A remaining challenge is to make valuable recommendation to specific researchers in specific fields in order to obtain more fruitful cross-disciplinary collaboration. Cross-disciplinary information hides in big data and the relationships between different fields are complicated, complex, and subtle. This paper proposes a method for cross-disciplinary collaboration recommendation (CDCR) to analyze cross-disciplinary collaboration patterns in scholarly big data, and recommend valuable research fields for possible cross-disciplinary collaboration. A cross-disciplinary discovery algorithm based on topic modeling is designed to extract potential research fields. Collaboration patterns are examined by analyzing the research field correlations. A recommendation algorithm is developed to provide a specific recommendation list of potential research fields according to the discovered cross-disciplinary collaboration patterns with researchers’ profiles. Evaluations conducted based on a real scholarly dataset demonstrate the effectiveness of the proposed method in recommending potentially valuable collaborations. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.future.2017.12.038 | Future Generation Computer Systems |
Keywords | Field | DocType |
Cross-disciplinary,Research collaboration recommendation,Research field discovery,Collaboration pattern,Scholarly big data | Data science,Computer science,Discipline,Topic model,Potential field,Big data,Distributed computing,Scientific method | Journal |
Volume | ISSN | Citations |
87 | 0167-739X | 3 |
PageRank | References | Authors |
0.42 | 21 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Liang | 1 | 125 | 10.04 |
| Xiaokang Zhou | 2 | 225 | 25.50 |
| Suzhen Huang | 3 | 3 | 0.76 |
| Hu, C. | 4 | 111 | 9.77 |
| Xuesong Xu | 5 | 15 | 1.59 |
| Jin, Q. | 6 | 233 | 33.40 |