作者 | AICon 全球人工智能开发与应用大会 策划 | 罗燕珊 编辑 | 宇琪 当大模型从通用技术探索深入产业场景，搜索、广告与推荐系统作为连接用户需求与业务价值的 核心链路，正迎来全链路智能重构。那么，生成式推荐真正落地后的关键挑战是什么？又应该如 何解决？ 近日 InfoQ《极客有约》X AICon 直播栏目特别邀请了 京东内容推荐架构负责人颜林 担任主持 人，和 荣耀 AI 算法专家冯晓东、京东算法总监张泽华、中科大计算机学院副教授王皓 一 起，在 AICon 全球人工智能开发与应用大会 2025 北京站 即将召开之际，共同探讨生成式推 荐的落地洞察。 部分精彩观点如下： 完整直播回放可查看： https://www.infoq.cn/video/0ViWrdqyQwNvO7TdQpyD 以下内容基于直播速记整理，经 InfoQ 删减。 行业真正做到端到端的统一 pipeline 仍有较大差距，更多工作还是在 pipeline 的单点与大模型 结合。 搜广推场景中的 scaling law 依然成立，并且仍在快速上升阶段。 低价值场景用小模型覆盖，高价值场景用大模型争取额外收益。 不应拘泥于某项技术 ...

Core Viewpoint - The article discusses the significant impact of large models on recommendation systems, emphasizing that these models have already generated tangible benefits in the industry rather than focusing on future possibilities or academic discussions [1]. Group 1: Impact of Large Models on Recommendation Systems - Large models have transformed the way knowledge is learned, shifting from a closed system reliant on internal data to an open system that integrates vast external knowledge [4]. - The structure of large models, typically based on transformer architecture, differs fundamentally from traditional recommendation models, which raises questions about whether they can redefine the recommendation paradigm [5]. - Large models have the potential to create a "new world" by enabling personalized content generation, moving beyond mere recommendations to directly creating tailored content for users [6]. Group 2: Knowledge Input Comparison - A comparison highlights that large models draw knowledge from an open world, while traditional systems rely on internal user behavior data, creating a complementary relationship [7]. - Large models possess advantages in knowledge quantity and embedding quality over traditional knowledge graph methods, suggesting they are the optimal solution for knowledge input in recommendation systems [8]. Group 3: Implementation Strategies - Two primary methods for integrating large model knowledge into recommendation systems are identified: generating embeddings from large language models (LLMs) and producing text tokens for input [10][11]. - The integration of multi-modal features through large models allows for a more comprehensive representation of item content, enhancing recommendation capabilities [13][15]. Group 4: Evolution of Recommendation Models - The exploration of large models in recommendation systems has progressed through three stages, from initial toy models to more industrialized solutions that significantly improve business metrics [20][24]. - Meta's generative recommendation model (GR) exemplifies a successful application of large models, achieving a 12.4% increase in core business metrics by shifting the focus from click-through rate prediction to predicting user behavior [24][26]. Group 5: Content Generation and Future Directions - The article posits that the most profound impact of large models on recommendation systems lies in the personalized generation of content, integrating AI creators into the recommendation process [28][29]. - Current AI-generated content still requires human input, but the potential for fully autonomous content generation based on user feedback is highlighted as a future direction [41][43]. Group 6: Industry Insights and Recommendations - The search and recommendation industry is viewed as continuously evolving, with the integration of large models presenting new growth opportunities rather than a downturn [45]. - The article suggests that the key to success in the next phase of recommendation systems lies in the joint innovation and optimization of algorithms, engineering, and large models [46].