  Harnessing Large Language Models (LLMs) for recommendation is rapidly
emerging, which relies on two fundamental steps to bridge the recommendation
item space and the language space: 1) item indexing utilizes identifiers to
represent items in the language space, and 2) generation grounding associates
LLMs' generated token sequences to in-corpus items. However, previous methods
exhibit inherent limitations in the two steps. Existing ID-based identifiers
(e.g., numeric IDs) and description-based identifiers (e.g., titles) either
lose semantics or lack adequate distinctiveness. Moreover, prior generation
grounding methods might generate invalid identifiers, thus misaligning with
in-corpus items. To address these issues, we propose a novel Transition
paradigm for LLM-based Recommender (named TransRec) to bridge items and
language. Specifically, TransRec presents multi-facet identifiers, which
simultaneously incorporate ID, title, and attribute for item indexing to pursue
both distinctiveness and semantics. Additionally, we introduce a specialized
data structure for TransRec to ensure generating valid identifiers only and
utilize substring indexing to encourage LLMs to generate from any position of
identifiers. Lastly, TransRec presents an aggregated grounding module to
leverage generated multi-facet identifiers to rank in-corpus items efficiently.
We instantiate TransRec on two backbone models, BART-large and LLaMA-7B.
Extensive results on three real-world datasets under diverse settings validate
the superiority of TransRec.
