Name
Title | ||
|---|---|---|
Ontology-based Semantic Similarity in Generating Context-aware Collaborator Recommendations |
Abstract | ||
|---|---|---|
Nowadays, the number of collaborative tools has increased significantly. This makes it difficult for users to find the collaborators that are most relevant to their needs among these tools. Besides, their needs can also be influenced by the context of collaboration (e.g., workplace, tools, and resources). This raises an issue: how to help users find their collaborators within the collaboration context. In our research, we propose an ontology-based semantic similarity and employ it in a collaboration context ontology to generate context-aware collaborator recommendations for users. In this paper, we present how to calculate and apply the semantic similarity in context-aware recommendation algorithms. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CSCWD49262.2021.9437647 | PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) |
Keywords | DocType | Citations |
Ontology-based semantic similarity, Context-aware recommendations, Collaborator, Collaboration context | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siying Li | 1 | 1 | 2.05 |
| Marie-Hélène Abel | 2 | 165 | 37.38 |
| Elsa Negre | 3 | 151 | 23.92 |