A Tourist Attractions Recommendation Model Based on Sequence Interest Extraction and Location Information Fusion

Abstract

Travel, as one way to relax oneself, has become the first choice for people to enjoy their body and mind in modern society. However, while facing lots of information, how to help users make better decisions on their next travel goals through their historical interest spots is a direction that needs further research in big data recommendation systems. In this thesis, we proposed the deep convolution and multi-head self-attention position network model. First, it extracts the user’s historical interest point feature information by convolutional neural network method, and then performs horizontal and vertical filtering. Next, it interacts the obtained information with the candidate attraction information, and extracts the location information of the historical interest sequence by the multi-head self-attention mechanism. Finally, the model does the attention mechanism of the candidate attraction by fusing the feature information of the location information. The final model achieves a deep fusion of user sequence interest and location feature information. We conducted detailed comparison experiments with the very popular models in the industry on different public datasets, and the results showed that our deep convolution and multi-head self-attention position network model has good performance.