Article: Smart tourism services and resource optimisation based on big data and knowledge graphs Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.44 pp.58 - 74 Abstract: To address the challenges of information overload and resource misallocation in the tourism industry, this paper proposes an intelligent service framework that integrates multi-source big data with knowledge graphs. By constructing a tourism-specific knowledge graph from the Yelp dataset (containing over 12,537 POIs and 45,821 users) and combining relational graph convolutional networks with long short-term memory models, the framework achieves precise personalised recommendations and dynamic resource optimisation. The proposed multi-task learning architecture jointly optimises recommendation accuracy and resource prediction performance. Extensive experiments show that the model significantly outperforms baseline methods, achieving a Precision@10 of 0.0914 and Recall@20 of 0.2542, along with a 21.73 root mean square error in flow prediction - demonstrating notable improvements in interpretability and robustness. This study provides an effective technical pathway for enhancing tourism service intelligence and operational efficiency. Inderscience Publishers - linking academia, business and industry through research

Title: Smart tourism services and resource optimisation based on big data and knowledge graphs

Authors: Jia Zuo; Jian Li

Addresses: School of Business, Sichuan University Jinjiang College, Meishan, 620860, China ' Physical Education College, Sichuan University Jinjiang College, Meishan, 620860, China

Abstract: To address the challenges of information overload and resource misallocation in the tourism industry, this paper proposes an intelligent service framework that integrates multi-source big data with knowledge graphs. By constructing a tourism-specific knowledge graph from the Yelp dataset (containing over 12,537 POIs and 45,821 users) and combining relational graph convolutional networks with long short-term memory models, the framework achieves precise personalised recommendations and dynamic resource optimisation. The proposed multi-task learning architecture jointly optimises recommendation accuracy and resource prediction performance. Extensive experiments show that the model significantly outperforms baseline methods, achieving a Precision@10 of 0.0914 and Recall@20 of 0.2542, along with a 21.73 root mean square error in flow prediction - demonstrating notable improvements in interpretability and robustness. This study provides an effective technical pathway for enhancing tourism service intelligence and operational efficiency.

Keywords: knowledge graph; smart tourism; resource optimisation; recommendation system; big data analysis.

DOI: 10.1504/IJICT.2025.150406

International Journal of Information and Communication Technology, 2025 Vol.26 No.44, pp.58 - 74

Received: 14 Sep 2025
Accepted: 31 Oct 2025
Published online: 12 Dec 2025 *

Title: Smart tourism services and resource optimisation based on big data and knowledge graphs

Keep up-to-date