  Classical sequential recommendation models generally adopt ID embeddings to
store knowledge learned from user historical behaviors and represent items.
However, these unique IDs are challenging to be transferred to new domains.
With the thriving of pre-trained language model (PLM), some pioneer works adopt
PLM for pre-trained recommendation, where modality information (e.g., text) is
considered universal across domains via PLM. Unfortunately, the behavioral
information in ID embeddings is still verified to be dominating in PLM-based
recommendation models compared to modality information and thus limits these
models' performance. In this work, we propose a novel ID-centric recommendation
pre-training paradigm (IDP), which directly transfers informative ID embeddings
learned in pre-training domains to item representations in new domains.
Specifically, in pre-training stage, besides the ID-based sequential model for
recommendation, we also build a Cross-domain ID-matcher (CDIM) learned by both
behavioral and modality information. In the tuning stage, modality information
of new domain items is regarded as a cross-domain bridge built by CDIM. We
first leverage the textual information of downstream domain items to retrieve
behaviorally and semantically similar items from pre-training domains using
CDIM. Next, these retrieved pre-trained ID embeddings, rather than certain
textual embeddings, are directly adopted to generate downstream new items'
embeddings. Through extensive experiments on real-world datasets, both in cold
and warm settings, we demonstrate that our proposed model significantly
outperforms all baselines. Codes will be released upon acceptance.
