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With the thriving of pre-trained language model (PLM) widely verified invarious of NLP tasks, pioneer efforts attempt to explore the possiblecooperation of the general textual information in PLM with the personalizedbehavioral information in user historical behavior sequences to enhancesequential recommendation (SR). However, despite the commonalities of inputformat and task goal, there are huge gaps between the behavioral and textualinformation, which obstruct thoroughly modeling SR as language modeling viaPLM. To bridge the gap, we propose a novel Unified pre-trained language modelenhanced sequential recommendation (UPSR), aiming to build a unifiedpre-trained recommendation model for multi-domain recommendation tasks. Weformally design five key indicators, namely naturalness, domain consistency,informativeness, noise & ambiguity, and text length, to guide the text-itemadaptation and behavior sequence-text sequence adaptation differently forpre-training and fine-tuning stages, which are essential but under-explored byprevious works. In experiments, we conduct extensive evaluations on sevendatasets with both tuning and zero-shot settings and achieve the overall bestperformance. Comprehensive model analyses also provide valuable insights forbehavior modeling via PLM, shedding light on large pre-trained recommendationmodels. The source codes will be released in the future.

Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language