ID-centric Pre-training for Recommendation

Understand

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.