Enhanced User Interaction in Operating Systems through Machine Learning
  Language Models
Chenwei Zhang Wenran Lu Chunhe Ni Hongbo Wang Jiang Wu
With the large language model showing human-like logical reasoning and
understanding ability, whether agents based on the large language model can
simulate the interaction behavior of real users, so as to build a reliable
virtual recommendation A/B test scene to help the application of recommendation
research is an urgent, important and economic value problem. The combination of
interaction design and machine learning can provide a more efficient and
personalized user experience for products and services. This personalized
service can meet the specific needs of users and improve user satisfaction and
loyalty. Second, the interactive system can understand the user's views and
needs for the product by providing a good user interface and interactive
experience, and then use machine learning algorithms to improve and optimize
the product. This iterative optimization process can continuously improve the
quality and performance of the product to meet the changing needs of users. At
the same time, designers need to consider how these algorithms and tools can be
combined with interactive systems to provide a good user experience. This paper
explores the potential applications of large language models, machine learning
and interaction design for user interaction in recommendation systems and
operating systems. By integrating these technologies, more intelligent and
personalized services can be provided to meet user needs and promote continuous
improvement and optimization of products. This is of great value for both
recommendation research and user experience applications.