Research on Personalized Design and Recommendation Systems for Cultural and Creative Products Based on User Behavior Data

Personalized recommendation technology involves the process of soliciting user data and prescribing a user interest model and active recommendations for some users. Such kinds of creative products involve subjective judgments and intricate patterns in the aesthetics, and it is always difficult to encode algorithms that are capable of understanding and recommending these subjective aspects. In this paper, the beetle swarm optimization algorithm is incorporated into a refined deep neural network model called the beetle swarm-drive refined deep neural network (BS-RDNN) for the analysis of personalized design and recommendation systems for user behaviors. Information about user behavior and feedback was collected as part of this study. The data were preprocessed using Min-Max normalization. t-distributed stochastic neighbor embedding (t-SNE) is employed to reduce the dataset dimensions. The proposed method is discussed with other types of recommendation algorithms. The proposed method is implemented with the aid of Python software. This result proves that the BS-RDNN method has better performance in terms of precision (91.34%), accuracy (93.24%), F1-score (92.23%), recall (92.44%), AUC (91.42%), and overall satisfaction of the users. Therefore, the use of the suggested system to coordinate with the design ideas of different individuals can benefit the field of cultural and creative products.