Enhanced User Interaction in Operating Systems through Machine Learning Language Models

arXiv:2403.00806v1 Announce Type: cross
Abstract: 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. …

abstract agents application arxiv behavior b test build cs.ce cs.cl cs.cv cs.ir human human-like language language model language models large language large language model machine machine learning operating systems reasoning recommendation systems test through type understanding virtual