Research on dynamic tourism route recommendation based on deep learning algorithm

Jianyun Li

doi:10.1117/12.2636782

8 May 2022 Research on dynamic tourism route recommendation based on deep learning algorithm

Jianyun Li

Proceedings Volume 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）; 122490W (2022) https://doi.org/10.1117/12.2636782
Event: 2022 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 2022, Xiamen, China

Abstract

With the development and prosperity of tourism, outbound tourism is becoming more and more popular. When people are ready to travel, the formulation of tourism routes is an important and key step. Although people can check the relevant travel guides or collect relevant information online, it is not only a waste of time and inefficient, but also the information found will not be consistent with their current needs. With the progress of the times, deep learning has developed rapidly in recent years, especially in image and text processing. This brings new inspiration to the research of recommendation system. This paper studies the dynamic tourism route recommendation based on the deep learning algorithm. The dynamic tourism route recommendation can be effectively solved through the obtained data, the established model and algorithm. Further, through the experimental comparison with different variants of the depth model, the experimental results show that the combination of the model also has better recommendation performance than other variants of the depth model, The effectiveness of the model combination is verified. This has laid a good foundation for the promotion and use of tourism service recommendation system.

Citation Download Citation

Jianyun Li "Research on dynamic tourism route recommendation based on deep learning algorithm", Proc. SPIE 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 122490W (8 May 2022); https://doi.org/10.1117/12.2636782

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KEYWORDS

Data modeling

Neural networks

Neurons

Image processing

Algorithm development

Filtering (signal processing)

Feature extraction

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