In this article, a novel data augmentation method is developed for Meta-Services based on large language models (LLMs) and requirement-functional-logical-physical (RFLP) models, referred to as generative LLM-driven data augmentation (GLIDE), and an application of GLIDE to e-commerce recommendation systems is presented. First, the framework of GLIDE is designed under Meta-Services, including a generalized encoder and decoder architecture, as well as designers, which captures the idea of “designers in the loop” in Meta-Services. Furthermore, the generalized encoder and decoder architecture is established based on RFLP models in systems engineering, aiming to translate requirements and knowledge into prompts and obtain ideal LLM outputs under physical constraints. Subsequently, the application of the GLIDE method to e-commerce recommendation systems is given in detail, which is committed to enriching users’ behavioral data and providing an effective way to decouple data augmentation and recommendation modules. Finally, recommendation experiments are performed on real-world and synthetic behavior data, and the empirical results demonstrate the effectiveness of our GLIDE method.

In the era of Industry 5.0 and the emerging metaverses, this study proposes Meta-Services, a novel paradigm that evolves a traditional service system into a multistage ecosystem. In the proposed paradigm, we develop a parallel intelligence-based management framework that enables stakeholders to control and manage the complex dynamics of service delivery in the metaverse. The management framework involves the following three stages: 1) service systems in metaverses; 2) elementary parallel service systems; and 3) parallel intelligence empowered ecosystems. The third stage evolves from the first two stages by incorporating the approach of “designer in the loop”, contributing to more human-centered, adaptive, and resilient service systems. Moreover, a case study in the retail sector is provided to validate the effectiveness of the proposed paradigm and show its practical potential.

SUMMARY: Cox-nnet is a neural-network-based prognosis prediction method, originally applied to genomics data. Here, we propose the version 2 of Cox-nnet, with significant improvement on efficiency and interpretability, making it suitable to predict prognosis based on large-scale population data, including those electronic medical records (EMR) datasets. We also add permutation-based feature importance scores and the direction of feature coefficients. When applied on a kidney transplantation dataset, Cox-nnet v2.0 reduces the training time of Cox-nnet up to 32-folds (n =10 000) and achieves better prediction accuracy than Cox-PH (P<0.05). It also achieves similarly superior performance on a publicly available SUPPORT data (n=8000). The high efficiency and accuracy make Cox-nnet v2.0 a desirable method for survival prediction in large-scale EMR data. AVAILABILITY AND IMPLEMENTATION: Cox-nnet v2.0 is freely available to the public at https://github.com/lanagarmire/Cox-nnet-v2.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.