Optimization of Rural Smart Tourism Service Model with Internet of Things

Abstract

Rural smart tourism is a new tourism form supported by information technology, especially the Internet of Things technology. Smart tourism not only makes it easier for tourists to get diverse tourism information, but it also helps attractive sites improve their service capabilities. The optimization of the rural smart tourist service model, which is explored in this work, includes personalized recommendations of scenic sites. The main research contents of the paper are as follows: (1) A new algorithm for recommending tourism destinations with domain adaptability has been suggested. In the field of personalized recommendation of tourist attractions, most of the target domain data are unlabeled, and the model cannot be trained. When it comes to training, however, there exist source domain data sets that are completely labeled and can be used as a supplement to the target domain training data sets. (2) A personalized recommendation algorithm for tourist attractions with deep migration has been studied. Owing to the distribution difference between target data and source data, an adaptive layer is added to the convolutional neural network. The domain loss is then minimized to achieve deep feature transfer, and the model can then be trained to find scenic areas of interest to the user. Finally, the recommendation for tourist destinations with deep migration is done by examining the relationship between the target user and other users.