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.

翻译：经典序列推荐模型通常采用ID嵌入来存储从用户历史行为中学习的知识并表征物品。然而，这些唯一ID难以迁移至新领域。随着预训练语言模型的蓬勃发展，一些先驱工作采用PLM进行预训练推荐，其中模态信息（如文本）通过PLM被视为跨领域的通用信息。遗憾的是，在基于PLM的推荐模型中，ID嵌入中的行为信息仍被验证比模态信息更具主导性，从而限制了这些模型的性能。本文提出了一种新颖的以ID为中心的推荐预训练范式（IDP），该范式直接将预训练域中学习到的信息性ID嵌入迁移至新域中的物品表征。具体而言，在预训练阶段，除了基于ID的序列推荐模型外，我们还构建了一个由行为信息和模态信息共同训练的跨域ID匹配器（CDIM）。在微调阶段，新域物品的模态信息被视为由CDIM建立的跨域桥梁。我们首先利用下游领域物品的文本信息，通过CDIM从预训练域中检索行为与语义相似的物品。随后，直接采用这些检索到的预训练ID嵌入（而非特定的文本嵌入）来生成下游新物品的嵌入。通过在真实数据集上进行的冷启动与热启动场景下的广泛实验，我们证明所提出的模型显著优于所有基线方法。相关代码将在论文被接收后发布。