This study investigates recommendations for interdisciplinary research collaboration based on the weak ties theory. Contents-based features are proposed to recommend interdisciplinary collaboration considering that some researchers who have shown a preference for interdisciplinary collaboration could be connected even if they have dissimilar research profiles. Therefore, we inferred the preference of interdisciplinary research collaboration for every researcher, and considered features such as highlighting dissimilar researchers depending on their preferences. The features are designed to have typical similarity measures when the researchers do not prefer interdisciplinary research collaboration. We evaluated our proposed features with the baseline features of patent application datasets and the former methods outperformed the latter methods.
|Title of host publication||Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017|
|Editors||Jana Diesner, Elena Ferrari, Guandong Xu|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||2|
|Publication status||Published - 2017 Jul 31|
|Event||9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia|
Duration: 2017 Jul 31 → 2017 Aug 3
|Name||Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017|
|Other||9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017|
|Period||17/7/31 → 17/8/3|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2016R1A2A1A05005270).
© 2017 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems