Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate cold start current tourism personalized system. First, a context based model (CPRM) established by using labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. interest point are improved mining information in unstructured text. Then, framework on convolutional neural network (IPRC) established. semantic emotional comment text extracted identify user preferences, score points target location predicted combined with influence factors geographical location. Finally, real datasets adopted evaluate above two models their performance solving problem.