A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots

This paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. Furthermore, word segmentation and word vector transformation are carried out through Jieba word segmentation tool and Skip-gram model, the semantic information between different data is deeply characterized, and the problem of very high vector sparsity is solved. Then, the corresponding features are obtained by using the feature extraction ability of DNN's in-depth learning. On this basis, the user's score on tourism service items is predicted through the model until a personalized recommendation list is generated. Finally, through simulation experiments, the recommendation accuracy and average reciprocal ranking of the proposed algorithm model and the other two algorithms in three different databases are compared and analyzed. The results show that the overall performance of the proposed algorithm is better than the other two comparison algorithms.