AI 中文总结
本文提出情境充分性理论，区分相关与不相关情境，通过全因子实验证明相关情境提升推荐适当性，并引入情境充分性前沿以确定最小相关集，推动个性化从增加数据转向精准识别需求。
AI 中文摘要
个性化长期以来依赖客户数据来推断个体可能重视的内容。我们将此称为客户证据：客户的历史行为和偏好。生成式AI通过允许提供者在生成响应时提供变化的即时情境信息，而无需预先编码所有条件，从而扩展了个性化。我们将提供者侧的情境定义为关于当前可能、允许或建议的信息。这种灵活性带来了一个新问题：一旦情境变得易于提供，更多并不一定更好。我们发展了一个情境充分性理论，其中情境与客户当前意图的相关性比其数量更重要。该理论识别了四种状态：不足、充分、饱和和干扰，并引入了情境充分性前沿以定位最小相关集。在一家大型家居零售商的生成式推荐器进行的全因子实验中，相关情境提高了适当性，而不相关情境则降低了适当性并破坏了检索稳定性。该框架将个性化从提供更多情境转向识别当前交互实际所需，并在整个服务过程中强制执行约束。
英文摘要
Personalization has long relied on customer data to infer what an individual is likely to value. We call this customer evidence: the customer's historical behavior and preferences. Generative AI extends personalization by allowing providers to supply changing situational information at the moment a response is produced, without encoding every condition in advance. We define this provider-side context as information about what is possible, permitted, or advisable now. This flexibility creates a new problem: once context becomes easy to supply, more is not necessarily better. We develop a theory of context sufficiency in which the relevance of context to the customer's current intent matters more than its volume. The theory identifies four states, insufficiency, sufficiency, saturation, and interference, and introduces the Context-Sufficiency Frontier to locate the minimal relevant set. In a full-factorial experiment with a generative recommender at a large home-furnishing retailer, relevant context improved appropriateness, while irrelevant context reduced it and destabilized retrieval. The framework shifts personalization from supplying more context toward identifying what the current interaction actually requires and enforcing constraints throughout the service process.
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