arXiv：cs.AI（全量分类）· Merieme Askour, Ayoub Merimi·· 5 小时前AI 评分34
生成式 AI 个性化中的上下文充分性边界：更多数据未必更好
When More Data Is Not Enough: The Context-Sufficiency Frontier in Generative AI Personalization
AI 导读
研究提出"上下文充分性"理论，指出生成式 AI 个性化中上下文与用户当前意图的相关性比数量更重要，并划分不足、充分、饱和、干扰四种状态，引入"上下文充分性边界"定位最小相关上下文集。在一家大型家居零售商的全因子实验中，相关上下文提升了推荐适当性，无关上下文则降低适当性并破坏检索稳定性。该框架主张个性化应从提供更多上下文转向识别当前交互真正需要什么。
正文
Abstract: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.
| Comments: | PREPRINT - SUBMITTED TO JOURNAL OF SERVICE RESEARCH (JSR) |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| MSC classes: | 68T50 (Primary), 68P20, 91B42, 91B44 |
| ACM classes: | H.3.3; I.2.7; J.4 |
| Cite as: | arXiv:2610.00654 [cs.AI] |
| (or arXiv:2610.00654v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00654 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Merieme Askour [view email]
[v1]
Wed, 30 Sep 2026 19:54:26 UTC (1,722 KB)
来源：arXiv：cs.AI（全量分类） · arxiv.org