  Traditional recommendation systems are subject to a strong feedback loop by
learning from and reinforcing past user-item interactions, which in turn limits
the discovery of novel user interests. To address this, we introduce a hybrid
hierarchical framework combining Large Language Models (LLMs) and classic
recommendation models for user interest exploration. The framework controls the
interfacing between the LLMs and the classic recommendation models through
"interest clusters", the granularity of which can be explicitly determined by
algorithm designers. It recommends the next novel interests by first
representing "interest clusters" using language, and employs a fine-tuned LLM
to generate novel interest descriptions that are strictly within these
predefined clusters. At the low level, it grounds these generated interests to
an item-level policy by restricting classic recommendation models, in this case
a transformer-based sequence recommender to return items that fall within the
novel clusters generated at the high level. We showcase the efficacy of this
approach on an industrial-scale commercial platform serving billions of users.
Live experiments show a significant increase in both exploration of novel
interests and overall user enjoyment of the platform.
