Context relevance matters more than volume in generative ai personalization
When More Data Is Not Enough: The Context-Sufficiency Frontier in Generative AI Personalization
Artificial IntelligenceMachine Learning
Summary
Personalization usually depends on a lot of customer data to guess what someone likes. The authors show that in generative AI, adding more situational information (context) isn’t always better. They found that a small, relevant set of context helps recommendations, while too much or irrelevant context makes them worse. Their work helps companies know how much and what kind of information to use when personalizing AI responses.
What this means in practice
- •For ecommerce recommendation teams: Improve product suggestions by using only context relevant to a shopper’s current intent, reducing noise and increasing recommendation appropriateness.
- •For customer support engineers: Design AI agents that limit situational context to what is needed for accurate and stable responses, avoiding overload and irrelevant data.
Tested on one dataset.
Authors
Merieme Askour, Ayoub Merimi
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.