| Title | A Recommendation Algorithm for Collaborative Conceptual Modeling Based on Co-occurrence Graph |
| Authors | Fu, Kai Wang, Shijun Zhao, Haiyan Zhang, Wei |
| Affiliation | Peking Univ, Sch EECS, Inst Software, Beijing 100871, Peoples R China. Minist Educ China, Key Lab High Confidence Software Technol, Beijing, Peoples R China. |
| Keywords | Collaborative Conceptual Modeling Recommendation Association Rule Mining Co-occurrence Graph SYSTEMS |
| Issue Date | 2015 |
| Publisher | REQUIREMENTS ENGINEERING IN THE BIG DATA ERA |
| Citation | REQUIREMENTS ENGINEERING IN THE BIG DATA ERA.Wuhan, PEOPLES R CHINA,2015/1/1,558(51-63). |
| Abstract | Conceptual models are models used to describe objects or systems in the real world. The quality of a conceptual model heavily depends on the domain knowledge and modeling experience of the individual modeler. Collaborative conceptual modeling is an effective way of building models by taking advantage of collective intelligence. This paper proposes a Co-occurrence Graph based Recommendation Algorithm (CGRA) to implement the collaborative mechanism of conceptual modeling systems. CGRA, inspired by association rule mining algorithm, is an incremental data updating algorithm. The computational complexity of CGRA is much lower than that of the traditional association rule mining based algorithms, while the recommendation effectiveness of these two are almost the same in our collaborative conceptual modeling system, which is revealed by the experiments we have conducted. |
| URI | http://hdl.handle.net/20.500.11897/446367 |
| ISSN | 1865-0929 |
| DOI | 10.1007/978-3-662-48634-4_4 |
| Indexed | EI CPCI-S(ISTP) |
| Appears in Collections: | 信息科学技术学院 |
