Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using Apriori algorithm

Song, Yang; He, Yingwei

doi:10.1007/s00500-023-09330-2

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…The tourism industry offers distinct benefits for a country such as economic growth, revenue generation, create jobs, and provide benefits to the local communities [1,2]. Social tourism [3] is the fastest growing field in which building of positive relationship between the local host and tourist is most important factor.…”

Section: Introductionmentioning

confidence: 99%

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

Kumar,

Goyal,

Sharma

2024

Int. j. inf. tecnol.

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The travel industry's contribution to sustainable development is noteworthy, since it offers travel experiences that are mutually beneficial to both local populations and tourists. But in order to maximize multiple resources in social tourism, an intelligent information system (IIS) is necessary for giving valuable tourism recommendations. Such systems use cutting-edge technologies like artificial intelligence, machine learning, and data analytics to improve the social tourism sector's overall efficacy and efficiency. The proposed intelligent information recommender system (IIRS) combines a number of different components to handle a number of tourism-related issues, such as impact assessment, community involvement, travel planning, and destination selection with the purpose of promoting sustainable social tourism. Fuzzy C-means clustering (FCM) is used in the proposed system for extraction of features. After extraction of features, the model is trained and tested using ensemble machine learning classifiers such as decision tree (DT) and extreme gradient boosting (XGB). Lastly, the evaluation of the system can be done with distinguished parameters such as accuracy, precision, recall, and F1-score.

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Section: Introductionmentioning

confidence: 99%

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

Kumar,

Goyal,

Sharma

2024

Int. j. inf. tecnol.

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“…This system introduced Apriori for user behavior analysis, resulting in customized recommendation content. This system had been validated after method validation, which was better than traditional recommendation methods such as decision trees and linear regression [11]. Clustering algorithms can perform clustering analysis on textual information to provide users with decision-making.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Similarity Fusion based Travel Interest Point Recommendation Algorithm in Youth Users’ Study Tours

Xing,

Wang

2024

SCPE

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Study tours for adolescent users are somewhat contemporary and traditional methods, such as questionnaires cannot meet their psychological expectations. To bring a better experience to teenagers’ study tour, a Latent Dirichlet Allocation (LDA) theme model was used to mine teenage users and their interest points, and then the similarity between LDA and the check-in matrix was calculated and fused. Based on this, an RT-CNN model was built for deep feature extraction of review information, and point-of-interest recommendation was performed by fusing similarity, check-in behavior, and geographic location. The RT-CNN model had an accuracy of 92.7%, a recall of 87.1%, a Mean Absolute Error (MAE) value of 4.2%, a Root Mean Square Error (RMSE) of 4.8%, and F1 values of 89.2% and 88.7% in the two datasets. The new model in this experiment has high accuracy in making interest point recommendations and has a good overall performance.

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An Innovative Design of SH System using T and L Techniques

Ramesh,

Sathish Kumar,

Das

et al. 2024

2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

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Toward an intelligent tourism recommendation system based on artificial intelligence and IoT using Apriori algorithm

Cited by 3 publications

References 42 publications

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

An intelligent information recommender system (IIRS) for next generation sustainable social tourism industry

The Impact of Similarity Fusion based Travel Interest Point Recommendation Algorithm in Youth Users’ Study Tours

An Innovative Design of SH System using T and L Techniques

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