Research on Intelligent Management System for Infant and Child Care Services Based on Multi Neighborhood Perception Optimization Algorithm

This article explores web service recommendation algorithms for heterogeneous environments by incorporating a time dimension into datasets, transforming static data into dynamic elements. This approach improves both the accuracy of service quality predictions and the speed of service recommendations, enhancing overall efficiency. The study introduces the DBSLF algorithm, which integrates cross-platform data features while prioritizing privacy protection. By fusing user service data from diverse edge server platforms, the algorithm provides more precise recommendations through multi-platform data analysis. The algorithm’s effectiveness is validated through experiments with varying data densities, significantly reducing the mean absolute error (MAE) for missing Quality of Service (QoS) values. To address service recommendation challenges in single-platform heterogeneous environments, the concept of continuous time slices is introduced, utilizing historical data to predict missing QoS values. Additionally, a privacy-preserving, cross-platform recommendation algorithm is proposed, leveraging latent factor models, density-based clustering, and matrix decomposition techniques. This approach enhances prediction accuracy, mitigates cold start issues, and contributes to smart transportation networks by improving road congestion predictions and enhancing daily travel experiences.