Operating systems (OSes) are foundational to computer systems, managing
hardware resources and ensuring secure environments for diverse applications.
However, despite their enduring importance, the fundamental design objectives
of OSes have seen minimal evolution over decades. Traditionally prioritizing
aspects like speed, memory efficiency, security, and scalability, these
objectives often overlook the crucial aspect of intelligence as well as
personalized user experience. The lack of intelligence becomes increasingly
critical amid technological revolutions, such as the remarkable advancements in
machine learning (ML).
Today's personal devices, evolving into intimate companions for users, pose
unique challenges for traditional OSes like Linux and iOS, especially with the
emergence of specialized hardware featuring heterogeneous components.
Furthermore, the rise of large language models (LLMs) in ML has introduced
transformative capabilities, reshaping user interactions and software
development paradigms.
While existing literature predominantly focuses on leveraging ML methods for
system optimization or accelerating ML workloads, there is a significant gap in
addressing personalized user experiences at the OS level. To tackle this
challenge, this work proposes PerOS, a personalized OS ingrained with LLM
capabilities. PerOS aims to provide tailored user experiences while
safeguarding privacy and personal data through declarative interfaces,
self-adaptive kernels, and secure data management in a scalable cloud-centric
architecture; therein lies the main research question of this work: How can we
develop intelligent, secure, and scalable OSes that deliver personalized
experiences to thousands of users?

本文提出了 PerOS，一种具有大型语言模型（LLM）功能的个性化操作系统，旨在通过声明性界面、自适应内核和安全数据管理等方法，在可扩展的云中心架构中提供定制化的用户体验，以解决如何开发智能、安全、可伸缩的操作系统以向数千用户提供个性化体验的主要研究问题。

PerOS：云上的个性化自适应操作系统

PerOS: Personalized Self-Adapting Operating Systems in the Cloud

Providing a personalized user experience on information dense webpages helps
users in reaching their end-goals sooner. We explore an automated approach to
identifying user personas by leveraging high dimensional trajectory information
from user sessions on webpages. While neural collaborative filtering (NCF)
approaches pay little attention to token semantics, our method introduces
SessionBERT, a Transformer-backed language model trained from scratch on the
masked language modeling (mlm) objective for user trajectories (pages,
metadata, billing in a session) aiming to capture semantics within them. Our
results show that representations learned through SessionBERT are able to
consistently outperform a BERT-base model providing a 3% and 1% relative
improvement in F1-score for predicting page links and next services. We
leverage SessionBERT and extend it to provide recommendations (top-5) for the
next most-relevant services that a user would be likely to use. We achieve a
HIT@5 of 58% from our recommendation model.

基于高维度轨迹信息，我们通过使用 SessionBERT 方法来识别用户画像，提供个性化的网页用户体验和推荐服务。