OPTIMIZING TOURISM SERVICE INTELLIGENT RECOMMENDATION SYSTEM BY MULTI-AGENT REINFORCEMENT LEARNING FOR SMART CITIES DESTINATION

The tourism sector is in a state of continual evolution, marked by a growing demand from travellers for customized and individualized experiences within smart city destinations. In response to this evolving landscape, this research introduces an innovative approach to intelligent recommendation sys...

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Main Authors: Pannee Suanpang, Pitchaya Jamjuntr
Format: Article
Language:English
Published: Regional Association for Security and crisis management, Belgrade, Serbia 2023-11-01
Series:Operational Research in Engineering Sciences: Theory and Applications
Subjects:
Online Access:https://oresta.org/menu-script/index.php/oresta/article/view/648
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author Pannee Suanpang
Pitchaya Jamjuntr
author_facet Pannee Suanpang
Pitchaya Jamjuntr
author_sort Pannee Suanpang
collection DOAJ
description The tourism sector is in a state of continual evolution, marked by a growing demand from travellers for customized and individualized experiences within smart city destinations. In response to this evolving landscape, this research introduces an innovative approach to intelligent recommendation systems for tourism services, utilizing Multi-Agent Reinforcement Learning (MARL). The proposed methodology employs a centralized critic and decentralized actor architecture to capture intricate interactions among agents, thereby generating recommendations of superior quality. Performance evaluation conducted on a real-world dataset demonstrates the method's superiority over existing approaches in terms of recommendation accuracy and diversity. Furthermore, this paper introduces a tourism service recommendation system based on MARL and assesses its efficacy using five distinct algorithms: Real, Random, DQN, DDPG, and MADDPG. Results indicate that the MADDPG algorithm surpasses other algorithms in providing reliable, efficient, and cost-effective services to tourists. MADDPG's capacity to learn and adapt to shifting user preferences and behaviours, facilitated by a centralized critic and decentralized actors learning from agent-environment interactions, enables it to adeptly navigate complex and dynamic environments. Moreover, the research delves into the implications of these findings for the tourism industry, drawing insights from feedback obtained from 400 respondents. The results reveal a high degree of user satisfaction with the optimized tourism service recommendation system in smart city destinations, consequently fostering a strong intention among users to revisit. This study represents a notable advancement in augmenting the tourism experience through sophisticated recommendation systems tailored for smart city destinations.
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spelling doaj.art-f49ea6198f14455d878e1334a9b281eb2024-04-23T19:49:30ZengRegional Association for Security and crisis management, Belgrade, SerbiaOperational Research in Engineering Sciences: Theory and Applications2620-16072620-17472023-11-0163OPTIMIZING TOURISM SERVICE INTELLIGENT RECOMMENDATION SYSTEM BY MULTI-AGENT REINFORCEMENT LEARNING FOR SMART CITIES DESTINATIONPannee Suanpang0Pitchaya Jamjuntr1Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand.King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand. The tourism sector is in a state of continual evolution, marked by a growing demand from travellers for customized and individualized experiences within smart city destinations. In response to this evolving landscape, this research introduces an innovative approach to intelligent recommendation systems for tourism services, utilizing Multi-Agent Reinforcement Learning (MARL). The proposed methodology employs a centralized critic and decentralized actor architecture to capture intricate interactions among agents, thereby generating recommendations of superior quality. Performance evaluation conducted on a real-world dataset demonstrates the method's superiority over existing approaches in terms of recommendation accuracy and diversity. Furthermore, this paper introduces a tourism service recommendation system based on MARL and assesses its efficacy using five distinct algorithms: Real, Random, DQN, DDPG, and MADDPG. Results indicate that the MADDPG algorithm surpasses other algorithms in providing reliable, efficient, and cost-effective services to tourists. MADDPG's capacity to learn and adapt to shifting user preferences and behaviours, facilitated by a centralized critic and decentralized actors learning from agent-environment interactions, enables it to adeptly navigate complex and dynamic environments. Moreover, the research delves into the implications of these findings for the tourism industry, drawing insights from feedback obtained from 400 respondents. The results reveal a high degree of user satisfaction with the optimized tourism service recommendation system in smart city destinations, consequently fostering a strong intention among users to revisit. This study represents a notable advancement in augmenting the tourism experience through sophisticated recommendation systems tailored for smart city destinations. https://oresta.org/menu-script/index.php/oresta/article/view/648Machine LearningMulti-Agent Reinforcement LearningRecommended SystemsService Recommendation SystemsSmart CitiesTourism Servies
spellingShingle Pannee Suanpang
Pitchaya Jamjuntr
OPTIMIZING TOURISM SERVICE INTELLIGENT RECOMMENDATION SYSTEM BY MULTI-AGENT REINFORCEMENT LEARNING FOR SMART CITIES DESTINATION
Operational Research in Engineering Sciences: Theory and Applications
Machine Learning
Multi-Agent Reinforcement Learning
Recommended Systems
Service Recommendation Systems
Smart Cities
Tourism Servies
title OPTIMIZING TOURISM SERVICE INTELLIGENT RECOMMENDATION SYSTEM BY MULTI-AGENT REINFORCEMENT LEARNING FOR SMART CITIES DESTINATION
title_full OPTIMIZING TOURISM SERVICE INTELLIGENT RECOMMENDATION SYSTEM BY MULTI-AGENT REINFORCEMENT LEARNING FOR SMART CITIES DESTINATION
title_fullStr OPTIMIZING TOURISM SERVICE INTELLIGENT RECOMMENDATION SYSTEM BY MULTI-AGENT REINFORCEMENT LEARNING FOR SMART CITIES DESTINATION
title_full_unstemmed OPTIMIZING TOURISM SERVICE INTELLIGENT RECOMMENDATION SYSTEM BY MULTI-AGENT REINFORCEMENT LEARNING FOR SMART CITIES DESTINATION
title_short OPTIMIZING TOURISM SERVICE INTELLIGENT RECOMMENDATION SYSTEM BY MULTI-AGENT REINFORCEMENT LEARNING FOR SMART CITIES DESTINATION
title_sort optimizing tourism service intelligent recommendation system by multi agent reinforcement learning for smart cities destination
topic Machine Learning
Multi-Agent Reinforcement Learning
Recommended Systems
Service Recommendation Systems
Smart Cities
Tourism Servies
url https://oresta.org/menu-script/index.php/oresta/article/view/648
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