Design of Intelligent Travel Recommendation Model Based on Big Data

With the rapid development of mobile internet and explosion of online travel information, tourists are overwhelmed by the massive amount of complicated and unstructured data from various online sources. This information overload severely impedes tourists from efficiently discovering personalized recommendations that match their interests and preferences. To address the urgent issue of information overload in the travel domain, designing an intelligent and personalized travel recommendation system based on advanced artificial intelligence techniques is crucial. Most existing recommendation systems rely heavily on simple user-item rating metrics and collaborative filtering approaches, which fail to fully understand each user's interests and capture the rich contextual information within massive amounts of user-generated data such as reviews and blogs. This research aims to develop a semantic-enhanced, deep learning based travel recommendation system to deal with the information explosion. Specifically, we utilize convolutional neural networks to extract multi-dimensional semantic features and perform fine-grained sentiment analysis on large-scale tourist review corpus. This allows comprehensive modeling of each user's interests and preferences. In addition, we employ Pearson correlation coefficient to measure interest similarities between tourists and generate accurate user profiles. Finally, by integrating both user profiles and rich user behavior data, we design a weighted rating prediction algorithm to generate personalized recommendations for attractions, restaurants, hotels, and various travel products. Experiments result shows that the traditional content-based recommendation methods can have a relatively high MAE of 0.70, while our proposed approach reduces the MAE to around 0.5. Compared with conventional content-based algorithms, our method can significantly reduce the error of travel recommendations and effectively enhance both relevance and accuracy. The proposed personalized and intelligent travel recommendation system can effectively help tourists discover products well-matched to their interests while alleviating the information overload problem.