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This invention describes a system that helps explain why certain things are recommended to you. When you get a recommendation, the system doesn't just show you the item; it also comes up with several possible reasons, or explanations, for why you might like it. It then picks the best explanation to show you, based on which explanations you've responded well to in the past, or how popular the recommended item was with other users when shown with those specific explanations. The claims narrow the invention to specifically using your past engagement with documents to rank these explanations.
Why it matters: Filed before advanced AI and large language models could efficiently generate diverse, personalized natural language explanations. Modern LLMs can now easily create and refine the candidate explanations, making the ranking and selection process more impactful and scalable.
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