Personalized Hotel Recommendation for Online Travel Platforms: An Information Entropy-Based Approach
Guofang Nan, Ruixuan Wang, Jiwei Luo, and Zhiyun Ji
International Journal of Electronic Commerce,
Volume 30, Number 4, 2026, pp. 599-619.
Abstract:
Online travel platforms aggregate a vast number of online reviews, reflecting users’ diverse attitudes and opinions about products. However, existing recommendation methods fail to accurately capture user preferences from these reviews, making it difficult to provide personalized recommendations. To overcome these limitations, we propose a novel information entropy-based hotel recommendation method based on online reviews to provide users with accurate recommendations. Specifically, aspect–opinion pairs and their sentiment polarities are first extracted from reviews. Then, sentiment entropy and semantic entropy are constructed to capture user preferences. Next, these features are incorporated into a user–hotel bipartite graph, where node2vec is applied to learn node representations. Last, the graph features, user features and hotel features are inputted into a BiGRU model for recommendation. Numerical experiments show that our method produces more accurate recommendations than several well-known baselines. Furthermore, we propose a semantic entropy-based review truncation method that significantly reduces computational costs while preserving recommendation quality.