PARAN: Persona-Augmented Review ANswering system on Food Delivery Review Dataset
Moonsoo Park, Jeongseok Yun, Bohyung Kim

TL;DR
This paper introduces PARAN, a two-stage prompting framework that infers user personas from reviews to generate personalized responses in food delivery services, improving relevance and engagement without fine-tuning models.
Contribution
The work presents a novel persona-augmented prompting method that infers explicit and implicit user personas from review texts for personalized response generation.
Findings
Enhanced response relevance and personalization.
Improved diversity and semantic consistency.
Effective without model fine-tuning.
Abstract
Personalized review response generation presents a significant challenge in domains where user information is limited, such as food delivery platforms. While large language models (LLMs) offer powerful text generation capabilities, they often produce generic responses when lacking contextual user data, reducing engagement and effectiveness. In this work, we propose a two-stage prompting framework that infers both explicit (e.g., user-stated preferences) and implicit (e.g., demographic or stylistic cues) personas directly from short review texts. These inferred persona attributes are then incorporated into the response generation prompt to produce user-tailored replies. To encourage diverse yet faithful generations, we adjust decoding temperature during inference. We evaluate our method using a real-world dataset collected from a Korean food delivery app, and assess its impact on…
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Taxonomy
TopicsPersona Design and Applications · Topic Modeling · Advanced Text Analysis Techniques
