Blockchain-Enabled Personalized Travel Recommendations with Semantic Search and Transparent Data
Yihan Huang, Jingxuan Liu, Sida Huang, Jialuoyi Tan, Xie He, Shuangyao Huang, Yuji Dong · 2025
The ever-increasing demand for different Internet of Things (IoT) platforms while satisfying the personalised quality of services has attracted both industry and academic interests. Especially for the personalised travel system of the tourism area, the existing tourism recommendation systems often struggle to provide highly relevant suggestions due to limitations in understanding complex and varied user preferences. Therefore, developing a personalized tourism recommendation platform that satisfies user privacy-protecting requirements presents a necessity. In this paper, we propose a blockchain-supported personalized tourism recommendation platform that integrates semantic search models with blockchain technology. Our approach combines semantic similarity and contextual similarity using advanced natural language processing (NLP) techniques, such as word embeddings, RoBERTa models, and attention mechanisms, to align entities effectively across multiple datasets. This integration ensures a deeper understanding of user inputs, overcoming the limitations of traditional keyword-based matching. Preliminary experiments suggest that our system significantly improves recommendation performance while maintaining transparency and accountability, offering a novel solution to the challenges facing current tourism recommendation systems.