Industrial recommender systems face two fundamental limitations under the log-driven paradigm: (1) knowledge poverty in ID-based item representations that causes brittle interest modeling under data sparsity, and (2) systemic blindness to beyond-log user interests that constrains model performance within platform boundaries. These limitations stem from an over-reliance on shallow interaction statistics and close-looped feedback while neglecting the rich world knowledge about product semantics and cross-domain behavioral patterns that Large Language Models have learned from vast corpora. To address these challenges, we introduce ReaSeq, a reasoning-enhanced framework that leverages world knowledge in Large Language Models to address both limitations through explicit and implicit reasoning. Specifically, ReaSeq employs explicit Chain-of-Thought reasoning via multi-agent collaboration to distill structured product knowledge into semantically enriched item representations, and latent reasoning via Diffusion Large Language Models to infer plausible beyond-log behaviors. Deployed on Taobao's ranking system serving hundreds of millions of users, ReaSeq achieves substantial gains: >6.0% in IPV and CTR, >2.9% in Orders, and >2.5% in GMV, validating the effectiveness of world-knowledge-enhanced reasoning over purely log-driven approaches.

翻译：工业推荐系统在日志驱动范式下面临两个根本性局限：(1) 基于ID的物品表征存在知识贫乏问题，导致在数据稀疏场景下的兴趣建模脆弱；(2) 系统对日志外用户兴趣存在固有盲区，将模型性能限制在平台边界内。这些局限源于对浅层交互统计和闭环反馈的过度依赖，而忽视了大型语言模型从海量语料中学到的关于产品语义和跨域行为模式的丰富世界知识。为应对这些挑战，我们提出ReaSeq——一个推理增强框架，其通过显式与隐式推理机制，利用大型语言模型中的世界知识同时解决上述两个局限。具体而言，ReaSeq通过多智能体协作进行显式思维链推理，将结构化产品知识蒸馏为语义增强的物品表征；同时通过扩散大型语言模型进行隐式推理，以推断合理的日志外行为。在服务数亿用户的淘宝排序系统中部署后，ReaSeq取得显著效果提升：IPV与CTR提升>6.0%，订单量提升>2.9%，GMV提升>2.5%，验证了世界知识增强推理相较于纯日志驱动方法的有效性。