Design of Individualized Tourism Service Recommendation Algorithm Based on User Behavior Analysis

This article's objective is to delve into and refine individualized travel service recommendation algorithms, enhancing recommendation accuracy and user satisfaction for travel service providers. Addressing the deficiencies of existing algorithms in managing sparse data, cold start issues, and capturing implicit user feedback, our study introduces a collaborative filtering (CF) recommendation algorithm rooted in user behavior analysis. Through comprehensive analysis of user behavior data and tourism service traits, we've established an efficient framework for processing and analyzing user behavior data, enabling precise identification of individual user needs. Experimental outcomes indicate that our proposed algorithm outperforms benchmark algorithms in terms of NDCG and other assessment metrics. Notably, when confronted with sparse data and cold start challenges, our research algorithm demonstrates heightened robustness and adaptability. Consequently, the individualized travel service recommendation algorithm presented in this article has yielded substantial improvements in recommendation accuracy and user experience, offering travel service providers an effective solution.