Prism: Faster, Better 'Why You'll Like This' Explanations for Recommendations

Kari Jaaskelainen

21 Nov 2025 — 1 min read

What if your app could not only pick what to recommend, but also explain why—clearly and fast? "The Oracle and The Prism" introduces Prism, a simple idea: separate the job of choosing items from the job of explaining them.

Here’s the trick: a large "Oracle" model generates rich notes about why an item fits you. A compact "Prism" model then turns those notes into short, personal explanations. Each model focuses on what it does best, avoiding the usual trade-offs when everything is trained end-to-end.

Human evaluations: the 140M-parameter Prism produced explanations rated more faithful and more personalized than its 11B-parameter teacher.
Efficiency: ~24× faster inference and ~10× lower memory use.

Why it matters: better, faster "why you'll like this" builds trust without pricey hardware—useful for shopping, news, music, or movies. Example: "We picked this sci-fi film for you because you liked space epics with strong female leads and 90s soundtracks."

Paper: https://arxiv.org/abs/2511.16543v1

Paper: https://arxiv.org/abs/2511.16543v1

Register: https://www.AiFeta.com

#AI #RecommenderSystems #ExplainableAI #LLM #MachineLearning #NLP #Efficiency

Meet GRACE: a moral governor for safer, more transparent AI

AI agents are getting powerful—so how do we make sure they do the right thing, not just the effective thing? Meet GRACE, a reason-based moral governor that keeps AI behavior aligned with human norms by separating moral reasoning from goal-driven decision-making. * Moral Module: uses deontic logic and explicit reasons

Why some fine-tuned LLMs miss phishing—and how to fix it

Not all fine-tuned LLMs spot phishing equally. A new study tests Llama 3.1 8B, Gemma 2 9B, and Mistral on high-stakes phishing detection—and uses SHAP and mechanistic interpretability to reveal why models do (or don’t) generalize. * Architecture × data diversity matters: Gemma 2 9B hits state-of-the-art performance (F1

AI that explains itself—by following the science

AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

TL;DR: Finding reliable answers in genomics is hard. GenomAgent turns one big AI into a coordinating team of specialists—and beats the current leader by 12% on a key benchmark. Genomic facts live across many databases. Standard chatbots struggle because they can’t flexibly query those sources. GeneGPT added

Read more

Meet GRACE: a moral governor for safer, more transparent AI

Why some fine-tuned LLMs miss phishing—and how to fix it

AI that explains itself—by following the science

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A