Enhancing Hyper-Personalization in Smart Glasses Design with a Knowledge Graph-Guided GAN Framework

Libo Wang

Keywords: Knowledge Graph, Generative Adversarial Networks, Hyper-Personalization, Smart Glasses

TL;DR: To build a GAN guided by knowledge graph, this work implements context adaptation and style adjustment for different users' semantic preferences to achieve super-personalized generation capabilities.

Abstract:

To build a GAN guided by knowledge graph, this study implements context adaptation and style adjustment for different users' semantic preferences to achieve super-personalized generation capabilities. The system consists of five modules and is simulated and experimentally operated by combining Python 3.13 IDLE with a Python simulator based on custom GPTs. The dataset is two sets of synthetic data that simulate image recognition and text generation respectively. The researcher uses single-group analysis to conduct semantic node perturbation tests and output response observations. The results show that the model is stable under multiple metrics, which proves that the framework has the ability to adjust the output of sentences and patterns in real time according to conditional nodes. This framework provides a design paradigm with closed-verification logic and semantic mapping consistency for the generation system of ultra-personalized smart glasses. And it can be extended to personalized dialogue and context-guided interface in the future, focusing on high-precision human-computer interaction adaptation at the semantic level.

Submission Number: 3