Journal of Engineering Research

Intelligent recommendation algorithms play a crucial role in enhancing the user experience on hotel reservation platforms and increasing the conversion rate. This study aims to explore the implementation methods of different types of intelligent recommendation algorithms and their impacts on the performance of hotel reservation platforms. Through experimental evaluation of several mainstream recommendation algorithms, it is found that the collaborative filtering-based approach performs particularly prominently in improving user satisfaction and personalized services. By analyzing user behavior data to optimize the recommendation strategy, the reservation success rate can be significantly increased. An effective recommendation system can not only enhance customer stickiness but also bring higher revenues to hotels. The accuracy of the recommendation system and the protection of user privacy remain important issues that need to be addressed.

Intelligent recommendation algorithm; Hotel reservation platform; Collaborative filtering; User satisfaction; Privacy protection