Tourism Product Recommendation Based on LSTM Model
Liping Yu, Yan Lu · Procedia Computer Science · 2025
With the booming development of the tourism industry, how to provide users with accurate and personalized tourism product recommendations has become a hot research topic. This article proposes a tourism product recommendation method based on the Long Short-Term Memory (LSTM) model. Firstly, analyze the current situation in the field of tourism product recommendation and the limitations of traditional recommendation methods, and explain the principle of LSTM model and its advantages in processing sequential data. Continuing with the detailed design of the LSTM based tourism product recommendation model architecture, including data collection and preprocessing, feature extraction, model construction, training, and evaluation. Through experimental verification, this method has significantly improved the accuracy and diversity of recommendations compared to traditional recommendation algorithms, and can better meet users’ needs for personalized tourism product recommendations, providing new ideas and effective solutions for product recommendations in the tourism industry.