  The emergence of Large Language Models (LLMs) has innovated the development
of dialog agents. Specially, a well-trained LLM, as a central process unit, is
capable of providing fluent and reasonable response for user's request.
Besides, auxiliary tools such as external knowledge retrieval, personalized
character for vivid response, short/long-term memory for ultra long context
management are developed, completing the usage experience for LLM-based dialog
agents. However, the above-mentioned techniques does not solve the issue of
\textbf{personalization from user perspective}: agents response in a same
fashion to different users, without consideration of their features, such as
habits, interests and past experience. In another words, current implementation
of dialog agents fail in ``knowing the user''. The capacity of well-description
and representation of user is under development. In this work, we proposed a
framework for dialog agent to incorporate user profiling (initialization,
update): user's query and response is analyzed and organized into a structural
user profile, which is latter served to provide personal and more precise
response. Besides, we proposed a series of evaluation protocols for
personalization: to what extend the response is personal to the different
users.
  The framework is named as \method{}, inspired by inscription of ``Know
Yourself'' in the temple of Apollo (also known as \method{}) in Ancient Greek.
Few works have been conducted on incorporating personalization into LLM,
\method{} is a pioneer work on guiding LLM's response to meet individuation via
the application of dialog agents, with a set of evaluation methods for
measurement in personalization.
