Large language models (LLMs) have shown impressive capabilities in natural
language understanding and generation. Their potential for deeper user
understanding and improved personalized user experience on recommendation
platforms is, however, largely untapped. This paper aims to address this gap.
Recommender systems today capture users' interests through encoding their
historical activities on the platforms. The generated user representations are
hard to examine or interpret. On the other hand, if we were to ask people about
interests they pursue in their life, they might talk about their hobbies, like
I just started learning the ukulele, or their relaxation routines, e.g., I like
to watch Saturday Night Live, or I want to plant a vertical garden. We argue,
and demonstrate through extensive experiments, that LLMs as foundation models
can reason through user activities, and describe their interests in nuanced and
interesting ways, similar to how a human would.
We define interest journeys as the persistent and overarching user interests,
in other words, the non-transient ones. These are the interests that we believe
will benefit most from the nuanced and personalized descriptions. We introduce
a framework in which we first perform personalized extraction of interest
journeys, and then summarize the extracted journeys via LLMs, using techniques
like few-shot prompting, prompt-tuning and fine-tuning. Together, our results
in prompting LLMs to name extracted user journeys in a large-scale industrial
platform demonstrate great potential of these models in providing deeper, more
interpretable, and controllable user understanding. We believe LLM powered user
understanding can be a stepping stone to entirely new user experiences on
recommendation platforms that are journey-aware, assistive, and enabling
frictionless conversation down the line.

这篇文章旨在通过大型语言模型对用户活动进行细致和透彻的理解，从而提高推荐平台上用户的个性化体验和更深层次的理解。通过提取兴趣轨迹并通过大型语言模型进行描述，我们证明了这种方法可以为用户提供深入、可解释和可控的理解，为用户体验提供新的可能性。