Analysis and Design of Tourism Recommender System for Religious Destinations of Nepal

Tan, Wenan; Shrestha, Deepanjal; Shrestha, Deepmala; Gaudel, Bijay; Jeong, Seung Ryul

doi:10.1109/icsecc51444.2020.9557574

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…The literature on recommender systems in the Nepalese context are limited. There are notably two authors [25][26][27][28][29][30] who have worked on the study of Tourist Recommender Systems for Nepalese tourism industries. In their papers [25][26][27][28][29][30], the author studied different aspects related to tweets and POI and the generation and distribution of geotagged tweets in Nepal, while [27] used volunteered geographic information and night-time light remote sensing data to identify tourism areas of interest [28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are notably two authors [25][26][27][28][29][30] who have worked on the study of Tourist Recommender Systems for Nepalese tourism industries. In their papers [25][26][27][28][29][30], the author studied different aspects related to tweets and POI and the generation and distribution of geotagged tweets in Nepal, while [27] used volunteered geographic information and night-time light remote sensing data to identify tourism areas of interest [28]. The other author [29] worked on the design of religious tourist recommender systems and conducted a preliminary analysis on the design of a Tourist Recommender System for Nepal [30].…”

Section: Literature Reviewmentioning

confidence: 99%

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Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

Shrestha,

Wenan,

Shrestha

et al. 2024

Computation

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This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four sub-models for data collection and machine learning. A structured survey is conducted with 2400 international and domestic tourists, featuring 28 major questions and 125 variables. The data are preprocessed, and significant features are extracted to enhance the accuracy and efficiency of the machine-learning models. These models are evaluated using metrics such as accuracy, precision, recall, F-score, ROC, and lift curves. A comprehensive database for Pokhara City, Nepal, is developed from various sources that includes attributes such as location, cost, popularity, rating, ranking, and trend. The machine-learning models provide intermediate categorical recommendations, which are further mapped using a personalized recommender algorithm. This algorithm makes decisions based on weights assigned to each decision attribute to make the final recommendations. The system’s performance is compared with other popular recommender systems implemented by TripAdvisor, Google Maps, the Nepal tourism website, and others. It is found that the proposed system surpasses existing ones, offering more accurate and optimized recommendations to visitors in Pokhara. This study is a pioneering one and holds significant implications for the tourism industry and the governing sector of Nepal in enhancing the overall tourism business.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

Shrestha,

Wenan,

Shrestha

et al. 2024

Computation

Self Cite

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“…Surveys showed that tourists increasingly visit religious destinations to experience the lifestyles, everyday culture and customs of the people they visit. Another piece of evidence presented by Wenan (2020) highlighted that Nepal has a diverse cultural and religious heritage that attracts tourists. Therefore, Nepal is a unique place that attracts religious tourists.…”

Section: Religious Tourism In Nepalmentioning

confidence: 99%

Strategic Imperatives of Sustainable Marketing of Religious Tourism in Nepal

Khadka,

Malviya

2023

KMC J.

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The growth of tourism industry presents both opportunities and challenges and requires effective marketing strategies to promote sustainable development. In this context, the study examines the current state and identifies sustainable marketing strategies for religious tourism in Nepal. The study is based on a qualitative research design and utilizes self-administered open-ended questions to collect data. It employs snowball sampling to select samples from two distinct groups: tourism professionals and entrepreneurs, who are acknowledged for their remarkable abilities and expertise in the field and have achieved high levels of professional recognition at the national level. The analysis involves identifying key themes related to the data and relevant literature using a thematic analytical approach. The study is limited due to the small sample size. Results of the study indicate that a balanced approach, which integrates economic, social, and environmental marketing initiatives, is crucial for the sustainable development of Nepal’s religious tourism industry. The study emphasizes the interdependent nature of these initiatives and their significant impact on the industry’s long-term viability. The study provides insights into the key marketing strategies and best practices of religious tourism for governments, tourism boards, entrepreneurs and stakeholders to drive long-term success and growth in Nepal.

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“…Besides numerous studies on tourism and satisfaction, the Nepalese literature lacked studies in the area of application of technology and tourism satisfaction. The studies that were traced mainly talked about technology applications in tourism with digital tourism security systems [30], Religious recommender systems [31], Sustainable tourism [32] Tourism ecosystem, and Tourism and e-commerce [33]. Studies were also discovered related to ICT implications in tourism, geotags and tourism points of interest, e-tourism, Nepal digital tourism framework, and mobile technology in tourism [34].…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-dimensional Analysis and Prediction Model for Tourist Satisfaction

2022

KSII TIIS

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This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naïve Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.

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Analysis and Design of Tourism Recommender System for Religious Destinations of Nepal

Cited by 4 publications

References 8 publications

Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

Strategic Imperatives of Sustainable Marketing of Religious Tourism in Nepal

Multi-dimensional Analysis and Prediction Model for Tourist Satisfaction

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