Article: Interest-aware and context-adaptive model for personalised travel route recommendation Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.29 pp.51 - 68 Abstract: With the growing demand for personalised tourism, route recommendation has become a key issue in intelligent travel. Existing methods face limitations in personalisation, temporal rhythm, and adaptability to dynamic environments. We propose an interest-aware and context-adaptive route recommendation model (ICRR). First, an interest-aware adaptive attention mechanism integrates user interest vectors into graph attention networks to enable personalised representations. Second, a temporal segmentation optimiser leverages LSTM and attention to capture temporal dependencies and solve the orienteering problem with time constraints, using adaptive perturbation search to avoid local optima. Finally, a dynamic route refinement mechanism models environmental factors through reinforcement learning for real-time route adjustment. Experiments show that ICRR outperforms baselines in user satisfaction, recommendation accuracy, and robustness, offering an efficient solution for smart tourism and intelligent transportation. Inderscience Publishers - linking academia, business and industry through research

Title: Interest-aware and context-adaptive model for personalised travel route recommendation

Authors: Chuanjun Liang; Xuelian Shang

Addresses: School of Information Engineering, Xinjiang Institute of Engineering, Ürümqi, Xinjiang, 830023, China ' School of Information Engineering, Xinjiang Institute of Engineering, Ürümqi, Xinjiang, 830023, China

Abstract: With the growing demand for personalised tourism, route recommendation has become a key issue in intelligent travel. Existing methods face limitations in personalisation, temporal rhythm, and adaptability to dynamic environments. We propose an interest-aware and context-adaptive route recommendation model (ICRR). First, an interest-aware adaptive attention mechanism integrates user interest vectors into graph attention networks to enable personalised representations. Second, a temporal segmentation optimiser leverages LSTM and attention to capture temporal dependencies and solve the orienteering problem with time constraints, using adaptive perturbation search to avoid local optima. Finally, a dynamic route refinement mechanism models environmental factors through reinforcement learning for real-time route adjustment. Experiments show that ICRR outperforms baselines in user satisfaction, recommendation accuracy, and robustness, offering an efficient solution for smart tourism and intelligent transportation.

Keywords: interest-aware attention; route optimisation; temporal modelling; reinforcement learning; personalised recommendation.

DOI: 10.1504/IJICT.2026.152580

International Journal of Information and Communication Technology, 2026 Vol.27 No.29, pp.51 - 68

Received: 20 Oct 2025
Accepted: 22 Nov 2025
Published online: 27 Mar 2026 *

Title: Interest-aware and context-adaptive model for personalised travel route recommendation

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