Abstract
How to plan a route within the scenic spot to satisfy different travel preferences was studied. Firstly, convolution-recurrent neural network (CNN-RNN) was leveraged to classify embed images and texts in travelogues and identify which landscape data were describing. Next, graph-based PhotoRank algorithm was used to select pictures with diversity and representativeness within each landscape. Finally, the association rules were employed to find the recommended routes for different needs of different travel groups. An experiment on seven popular scenic was conducted. The travel data were collected in Mafengwo. The results showed that the cross-modal analysis and context-related travel route recommendation method based on group intelligence data could truly depict the scenic spots from multiple angles, and the recommended context-related route could meet the specific needs of different groups.
| Translated title of the contribution | Cross-modal Crowd Sourced Data for Context-based Scenic Route Recommendation |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 22-28 |
| Number of pages | 7 |
| Journal | Journal of Zhengzhou University - Natural Science |
| Volume | 52 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 2020 |