ReaSeq：通过推理解锁世界知识用于序列建模
ReaSeq: Unleashing World Knowledge via Reasoning for Sequential Modeling
AI总结：
ReaSeq通过引入世界知识增强推理，提升推荐系统在物品表示和用户兴趣建模上的性能，实现IPV、CTR、订单和GMV的显著提升。
摘要：
工业推荐系统在日志驱动范式下面临两个根本性限制：（1）基于ID的物品表示的知识贫乏，导致在数据稀疏时兴趣建模脆弱；（2）对超出日志的用户兴趣缺乏系统性认知，限制了模型性能在平台边界内的表现。这些限制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庞大语料库中学习到的关于产品语义和跨领域行为模式的丰富世界知识。\n为了解决这些挑战，我们引入了ReaSeq，一个增强推理的框架，通过利用大型语言模型中的世界知识来解决这两个限制，通过显式和隐式推理。具体来说，ReaSeq通过多智能体协作进行显式链式推理，将结构化的产品知识转化为语义丰富的物品表示，并通过扩散大型语言模型进行隐式推理，推断出合理的超出日志行为。部署在淘宝的排名系统上，为数亿用户提供服务，ReaSeq实现了显著的提升：IPV和CTR提升超过6.0%，订单提升超过2.9%，GMV提升超过2.5%，验证了基于世界知识增强推理的有效性，优于纯粹日志驱动的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
