<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper aims to design a microsoft public service system for toilets in tourist attractions. Through the research and analysis of the adaptive recommendation algorithm, an adaptive recommendation algorithm model based on deep reinforcement is constructed. Using the algorithm model to analyze tourists’ demand data for the scenic toilet service system and tourists’ expectations of the system functions, a WeChat public service system with finding toilets as the core was established. The results show that the most important channel for tourists aged 13-17 to obtain toilet information is through the scenic spot staff and the information column, accounting for 40% and 42%, respectively, while tourists aged 66 and above mainly obtain toilet information through the scenic spot toilet slogan, accounting for 52%, and the older stage considers more the distance of the toilet from the departure place the number of visits in 200, and the middle-aged and young tourists consider more the function of toilet finding. The number of visits is 200, 180 times. This paper provides an effective reference for the design of the public service system of Wechat for toilets in tourist attractions, which is conducive to promoting the update and improvement of the system.</p>
</abstract>

This paper aims to design a microsoft public service system for toilets in tourist attractions. Through the research and analysis of the adaptive recommendation algorithm, an adaptive recommendation algorithm model based on deep reinforcement is constructed. Using the algorithm model to analyze tourists’ demand data for the scenic toilet service system and tourists’ expectations of the system functions, a WeChat public service system with finding toilets as the core was established. The results show that the most important channel for tourists aged 13-17 to obtain toilet information is through the scenic spot staff and the information column, accounting for 40% and 42%, respectively, while tourists aged 66 and above mainly obtain toilet information through the scenic spot toilet slogan, accounting for 52%, and the older stage considers more the distance of the toilet from the departure place the number of visits in 200, and the middle-aged and young tourists consider more the function of toilet finding. The number of visits is 200, 180 times. This paper provides an effective reference for the design of the public service system of Wechat for toilets in tourist attractions, which is conducive to promoting the update and improvement of the system.

Design of WeChat Public Service System for Toilets in Tourist Attractions Based on Adaptive Recommendation Algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper aims to design a microsoft public service system for toilets in tourist attractions. Through the research and analysis of the adaptive...

Design of WeChat Public Service System for Toilets in Tourist Attractions Based on Adaptive Recommendation Algorithm

Data publikacji: 04 paź 2023

Zakres stron: -

Otrzymano: 15 gru 2022

Przyjęty: 14 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00481

Słowa kluczowe
Adaptive recommendation, Deep reinforcement, Scenic spot toilet finding, Public service system, Distance location

© 2023 Qifang Yao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Design of WeChat Public Service System for Toilets in Tourist Attractions Based on Adaptive Recommendation Algorithm

Data publikacji: 04 paź 2023

Zakres stron: -

Otrzymano: 15 gru 2022

Przyjęty: 14 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00481

Słowa kluczoweAdaptive recommendation, Deep reinforcement, Scenic spot toilet finding, Public service system, Distance location

© 2023 Qifang Yao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Adaptive recommendation, Deep reinforcement, Scenic spot toilet finding, Public service system, Distance location