A Deep Learning-Based Recommender Model for Tourism Routes by Multimodal Fusion of Semantic Analysis and Image Comprehension

Tourism recommendation systems have tended to become popular in recent years. Due to the fact that tourism content is generally with the format of multimodal information, existing research works mostly ignored the fusion of various feature types. To deal with this issue, this paper resorts to multimodal fusion of semantic analysis and image comprehension, and proposes a novel deep learning-based recommender system for tourism routes. First, semantic analysis under tourism route search is conducted, in order to complete destination selection and process selection. Then, image comprehension of overall tourism route planning is conducted by establishing an end-to-end object recognition model. Finally, the previous two parts of characteristics are fused together to formulate an integrated recommender system with multimodal sensing ability. This thought is expected to bring a stronger ability for tourism route discovery. Empirically, operational efficiency and stability analysis are carried out on real-world data to evaluate the performance of the proposal. The experimental results show that it can achieve significant improvement in tourism route recommendation, can accurately capture user preferences, and can provide travel suggestions that meet user requirements.