Dynamic clustering and ranking fusion for tourism recommendations: A data-driven approach to 5A scenic spot evaluation

Abstract

In the tourism sector, tourists often face high-dimensional evaluation data, which makes informed decision-making challenging. To improve the effectiveness of tourism recommendations and address the challenges faced by Multi-Attribute Decision-Making (MADM) methods in handling high-dimensional evaluation data, this study proposes a method that combines dynamic clustering with ranking fusion techniques. First, a consistency-driven dynamic clustering algorithm is employed to identify and group evaluation attributes with similar ranking patterns across scenic spots. Second, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied within each cluster to obtain local rankings of scenic spots based on the corresponding evaluation information. Finally, the Borda count method is used to fuse the local rankings across different clusters and obtain a global ranking. Through an empirical application to 5A scenic spots in China, the proposed method demonstrates a high degree of consistency with existing tourism ratings. This study provides an effective recommendation framework for tourism management, particularly in scenarios involving high-dimensional and heterogeneous evaluation data, and demonstrates its potential practical value.