Attraction Recommendation and Itinerary Planning for Smart Rural Tourism Based on Regional Segmentation

As the rural tourism industry develops, effective attraction recommendations and planning are crucial for the tourist experience. Then, a rural scenic spot tourism recommendation and planning technology based on regional segmentation was proposed. The scenic area was divided into multiple grids based on tourist check-in behaviour, and the interest and influence of the scenic area were associated with the grid check-in behaviour. Content recommendation was achieved through two factors: popularity and regional location. And considering the sparsity of data in the recommendation, clustering algorithms were introduced to model tourist check-in behaviour based on factors such as time and regional location, and content recommendation was achieved through tourist preferences. In the performance analysis of recommendation models, the proposed model has an accuracy of 0.965 and 0.956 on the Gowalla and Yelp datasets, respectively, which is superior to other models. Comparing the recommendation loss performance of different models, the proposed model has an RMSE loss of 0.120 on the Gowalla dataset, which is superior to other models. In practical application analysis, when the recommended number is 5, the accuracy and recall of the proposed model are 0.138 and 0.069, respectively, which are superior to other models. In tourism itinerary planning, the overall planning time of the model is the shortest. Therefore, the proposed model has excellent application effects, and the research content provides important technical references for tourist travel and rural tourism destination planning.

@article{Chen2024,
  title     = {Attraction Recommendation and Itinerary Planning for Smart Rural Tourism Based on Regional Segmentation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ruiping Chen and Yanli Zhou and Dejun Zhang},
  doi       = {10.14569/IJACSA.2024.0150196},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150196}
}