জঞ্জ, থাকত উলী আর কি আর
• টি। ভারী।
O স্তো) আল্লাজীদ
Of) কাতার
O) ওনারা
Answers
The preference of destination selection directly affects the development trend of outbound tourism. Therefore, it is of great significance to predict the preference of destination selection. At present, a large number of scholars have carried out different degrees of research on the choice of tourism destination and achieved certain theoretical results. In reference [4], a hierarchical Bayesian network with multiple data sources is used to estimate and predict daily source destination tuples. The concept of O-D tuples is proposed. Vehicles in the road network are predicted and tracked by an advanced monitoring system. Aiming at the problem of obtaining the posterior probability of uncertain parameters, a multiprocess hierarchical Bayesian network mechanism in Gaussian space is developed. The model includes the level and trend components of future traffic volume. This method can meet the demand of forecasting and reduce the uncertainty in the process of estimation and prediction. In reference [5], a destination prediction method based on the initial part of the vehicle trajectory is proposed. The new trajectories are assigned to the most likely clusters, and the similarity grid driven by data is generated by obtaining the trajectory clustering describing user behavior. The final destination of the new trajectory is predicted by using the characteristics of the internal trajectories of the model cluster. The prediction accuracy of this method is high. However, the above methods do not consider the important characteristics of tourism destination selection preference, which leads to the problems of low prediction accuracy and comprehensive accuracy and long prediction time [6].
In view of the above problems, this paper puts forward a prediction method of tourism destination selection preference based on edge computing. It uses the edge calculation to construct the tourism destination selection preference characteristics, uses the random forest algorithm, selects the important features, uses the multiple logit selection model to obtain the tourists’ preference sequence for the choice of tourism destinations and sorts them, and obtains the tourism destination selection preference model. By calculating the weight value of tourism destination selection preference, the preference weight set of tourism destination selection is determined, and the tourism destination selection preference prediction is realized. The comprehensive accuracy of the tourism destination preference prediction method is good, which can effectively shorten the prediction time and improve the prediction accuracy [7].
The research contributions of the paper include the following:
(1) A prediction method based on the preference of tourist destination selection is proposed
(2) This paper uses edge computing technology to construct tourist destination selection preference features and uses a random forest algorithm to select important features and perform preliminary estimation and ranking
(3) Using the multiple logit selection model, the preferred sequence of tourists’ choice of tourist destinations is obtained and selected, and the model of tourist destination choice preference is obtained
(4) Determine the weight set of the tourist destination selection preference by calculating the weight value of the tourist destination selection preference and determine the tourist destination selection preference according to the link prediction method to realize the