用统一物品编码预训练大模型，实现跨域推荐零样本效果突破
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation
- 构建面向推荐任务的通用预训练模型，用物品层级编码提升跨域泛化
- 1.5B参数模型在8个数据集上超越7B参数大模型的零样本推荐表现
- 适合需要跨域推荐且无标注数据的场景，如新平台冷启动
基于大语言模型的推荐系统虽有进展，但因语言预训练与推荐任务本质不匹配，跨域泛化能力受限。现有方法依赖语言级知识，难以捕捉跨域动态的物品级用户兴趣。为此，我们提出RecBase，一种以推荐为导向、无领域偏见的基础模型。RecBase利用大规模异构跨域语料库，通过统一文本表示和特征映射增强跨域泛化能力。为对齐不同域的物品语义，引入统一物品分词器，将物品编码为层次化概念标识符，实现结构化表示与高效词表共享。模型采用自回归目标训练，以捕捉复杂的物品级序列模式。在8个真实世界数据集上，我们的1.5B参数模型在零样本与跨域推荐任务中达到或超过7B参数大模型的性能。
原文摘要 · Abstract (English)
Recent advances in LLM-based recommendation have shown promise, yet their cross-domain generalization is hindered by a fundamental mismatch between language-centric pretraining and the recommendation task. Existing methods, relying on language-level knowledge, fail to capture dynamic, item-level user interests across domains. To bridge this gap, we propose RecBase, a domain-agnostic foundational model pretrained with a recommendation-oriented objective. RecBase leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross-domain generalization. To further align item semantics across domains, we introduce a unified item tokenizer that encodes items into hierarchical concept identifiers, enabling structured representation and efficient vocabulary sharing. The model is trained using an autoregressive objective to capture complex item-level sequential patterns. On eight real-world datasets, our 1.5B-parameter model matches or surpasses the performance of LLM baselines up to 7B parameters in zero-shot and cross-domain recommendation tasks.
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