Personalized Recommendation Method for Tourism City Cultural Creative Products using Recurrent Neural Network with Aquila Optimization Algorithm

The Artificial Intelligence (AI) enables the smart tourist services and the products which emphasizes the experience of the customers. The AI-assisted smart tourism city cultural creative products which purposes to enhance the satisfaction of the customers and contributes the personalized recommendations. The cultural creative products and the solutions require to be tailored to develop a requirement through contributing exceptional experiences. Nevertheless, the product systems which significantly acquire the destination-based recommendation method and constrained customized options for integrating every traveller’s expectation. Hence, this research proposes the Recurrent Neural Network with Aquila Optimization Algorithm (RNN-AOA) approach for the estimation of smart tourism city cultural creative products. By integrating AOA, the RNN model becomes more robust and adaptable to a wide range of factors that affect cultural creative products in smart tourism cities. The z-score normalization technique is used in the preprocessing step to improve the excellence of a data which enhancing the accuracy for cultural creative product demand. The experimental outcomes demonstrates that the proposed RNN-AOA method attains a better accuracy of 0.996 respectively as compared to the existing method of RNN.