From destination image to regional embeddings

A multi-scale framework for cross-city tourism recommendation

Datos Bibliográficos

ID21699343
AutoresFeiyu Tong (Lanzhou Jiaotong University), 佟飞宇 佟飞宇 (Lanzhou Jiaotong University), Zhongrong Zhang (0000-0002-8176-7144, Lanzhou Jiaotong University, autor de correspondencia), Nengzhi Jin (Gansu Computing Center), 金能智 金能智 (Gansu Province Computing Center), Haobo Liang (0009-0003-0572-6150, Lanzhou Jiaotong University), Kehan Zhu (Lanzhou Jiaotong University)
Año2026
Páginas1-26
Fecha de publicación2026-06-24
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaCurrent Issues in Tourism (JOURNAL)
Identificadores de la revistaISSN: 1368-3500 • E-ISSN: 1747-7603
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/13683500.2026.2689449
OpenAlexW7165802041
IdiomaEN
Referencias citadas58

Regional heterogeneity poses a persistent challenge for cross-city tourism recommendation, as tourist behaviour and destination characteristics vary widely across cultural, economic, and spatial contexts. This study investigates how such regional differences shape visitor behaviour and proposes a novel recommendation framework, FPNRegionalRec (Feature Pyramid Network-based Regional Recommendation Framework), that explicitly models city-level contextual factors. Using large-scale user-generated reviews collected from Ctrip across six Chinese cities (Beijing, Shanghai, Wuhan, Nanjing, Hangzhou, and Suzhou), we analyze cross-city differences in experiential, temporal, and geographic patterns and incorporate them into an interpretable recommendation model. Experimental results show that FPNRegionalRec consistently outperforms mainstream baselines, including collaborative filtering, graph-based, and sequential recommendation models, particularly in culturally similar and high-traffic cities, while regional embeddings remain robust under cold-start conditions. By connecting destination image theory and tourist behaviour heterogeneity with computational modelling, this study provides an interpretable framework for cross-city tourism recommendation. The findings can help online travel platforms and destination management organisations adapt recommendation strategies across heterogeneous cities, identify context-specific behavioural signals, and support more targeted point of interest (POI) promotion and cold-start management

Destination Image · Mainstream · Point of interest · Tourism · Visitor pattern · Digital Marketing and Social Media · Diverse Aspects of Tourism Research · Recommender Systems and Techniques