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Conference Paper: Navigational Guidance – A Deep Learning Approach
| Title | Navigational Guidance – A Deep Learning Approach |
|---|---|
| Authors | |
| Keywords | Navigation guidance directed Steiner Tree graph neural network |
| Issue Date | 2021 |
| Publisher | Association for Information Systems. The Journal's web site is located at http://iceb.johogo.com/proceedings/ |
| Citation | Proceedings of the 21st International Conference on Electronic Business (ICEB 2021): Corporate Resilience through Electronic Business in the Post-COVID Era, Nanjing, China, 3-7 December 2021. In Proceedings of the International Conference on Electronic Business, v. 21, p. 304-311 How to Cite? |
| Abstract | The useful navigation guidance is favorable to considerably reducing navigation time. The navigation problems involved with multiple destinations are formulated as the Directed Steiner Tree (DST) problems over directed graphs. In this paper, we propose a deep learning (to be exact, graph neural networks) based approach to tackle the DST problem in a supervised manner. Experiments are conducted to evaluate the proposed approach, and the results suggest that our approach can effectively solve the DST problems. In particular, the accuracy of the network model can reach 95.04% or even higher. |
| Persistent Identifier | http://hdl.handle.net/10722/311284 |
| ISSN | 2020 SCImago Journal Rankings: 0.118 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Yen, BP | - |
| dc.contributor.author | Luo, Y | - |
| dc.date.accessioned | 2022-03-21T08:47:30Z | - |
| dc.date.available | 2022-03-21T08:47:30Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.citation | Proceedings of the 21st International Conference on Electronic Business (ICEB 2021): Corporate Resilience through Electronic Business in the Post-COVID Era, Nanjing, China, 3-7 December 2021. In Proceedings of the International Conference on Electronic Business, v. 21, p. 304-311 | - |
| dc.identifier.issn | 1683-0040 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/311284 | - |
| dc.description.abstract | The useful navigation guidance is favorable to considerably reducing navigation time. The navigation problems involved with multiple destinations are formulated as the Directed Steiner Tree (DST) problems over directed graphs. In this paper, we propose a deep learning (to be exact, graph neural networks) based approach to tackle the DST problem in a supervised manner. Experiments are conducted to evaluate the proposed approach, and the results suggest that our approach can effectively solve the DST problems. In particular, the accuracy of the network model can reach 95.04% or even higher. | - |
| dc.language | eng | - |
| dc.publisher | Association for Information Systems. The Journal's web site is located at http://iceb.johogo.com/proceedings/ | - |
| dc.relation.ispartof | Proceedings of the International Conference on Electronic Business (ICEB) | - |
| dc.subject | Navigation guidance | - |
| dc.subject | directed Steiner Tree | - |
| dc.subject | graph neural network | - |
| dc.title | Navigational Guidance – A Deep Learning Approach | - |
| dc.type | Conference_Paper | - |
| dc.identifier.email | Yen, BP: benyen@business.hku.hk | - |
| dc.identifier.email | Luo, Y: yuluo@hku.hk | - |
| dc.identifier.authority | Yen, BP=rp01121 | - |
| dc.identifier.hkuros | 332241 | - |
| dc.identifier.volume | 21 | - |
| dc.identifier.spage | 304 | - |
| dc.identifier.epage | 311 | - |
| dc.publisher.place | United States | - |