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到的知识并表示物品。然而，这些唯一ID难以迁移到新领域。随着预训练语言模型（PLM）的兴起，一些开创性工作采用PLM进行推荐预训练，其中模态信息（如文本）通过PLM被视为跨领域通用的信息。不幸的是，在基于PLM的推荐模型中，ID嵌入中的行为信息仍然被证实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面向ID的推荐预训练范式（IDP），该范式直接将预训练领域中学到的信息性ID嵌入迁移到新领域的物品表示中。具体而言，在预训练阶段，除了基于ID的推荐序列模型外，我们还构建了一个跨领域ID匹配器（CDIM），该匹配器通过行为信息和模态信息联合学习。在调优阶段，新领域物品的模态信息被视为由CDIM构建的跨领域桥梁。我们首先利用下游领域物品的文本信息，通过CDIM从预训练领域中检索行为相似和语义相似的物品。接着，直接采用这些检索到的预训练ID嵌入（而非某些文本嵌入）来生成下游新物品的嵌入。通过在真实世界数据集上的大量实验（涵盖冷启动和热启动场景），我们证明了所提出的模型显著优于所有基线方法。代码将在论文接收后公开。