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实战中的自适应推荐系统：推理、评估与系统设计
原标题：Presentation: Adaptive Recommenders in the Real World: Inference, Evals, and System Design
AI 摘要
Mallika Rao 在 InfoQ 演讲中分享构建自适应推荐系统的实践经验，指出真正的难点不在模型本身，而在于端到端系统设计。她强调推荐系统本质上是持续学习、适应和演进的反馈系统与分布式系统，需在延迟、成本、可观测性、实验、合规等现实约束下运行，并认为评估比建模更难，是必须作为一等公民对待的关键环节。
以上摘要由 AI 生成，可能存在误差。事实请以原文为准。
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Transcript Mallika Rao: I'm very excited to be talking about one of my very close to heart topics, adaptive recommenders. When people hear the phrase, recommendation systems, typically they're thinking about models, maybe the stages that go into it, like ranking, retrieval systems, embeddings, your stores, offline, metrics, and all of that good stuff. Maybe recently, perhaps large language models. After spending many years in the area of search and discovery, personalization, recommendation syst
发布时间：2026-09-26 19:00
抓取时间：2026-09-26 19:59
来源机构：InfoQ