Re2A: Situated Conversational Recommendation via Rubric-based Preference Reasoning and Alignment

2026-09-16

AI Summary

本文提出Re2A框架，通过基于标准的偏好推理和对齐方法解决场景化对话推荐中理解用户偏好与生成合适响应的难题。

Abstract

Real-world recommendation scenarios are commonly grounded in shared physical environments during user-recommender interactions. This motivates situated conversational recommendation (SCR), a complex task requiring recommender assistants to jointly reason over dialogue history, co-observed scenes, and in-scene item attributes. However, current approaches struggle with this setting due to two intertwined challenges: accurately understanding situated user preferences throughout the conversation and generating responses that simultaneously satisfy user needs and grounded situations. To this end, we propose Re2A, a framework that formulates SCR as a structured reason-then-align process. We introduce rubric-based preference reasoning, which uses automated rubrics to guide the model toward producing explicit preference states. Based on these states, we propose a preference-conditioned optimization to align response generation with dual objectives: user preference satisfaction and situation consistency. Extensive experiments on two SCR datasets demonstrate that Re2A consistently outperforms state-of-the-art methods, delivering more precise, context-aware conversational recommendations. Our code is available at https://github.com/DongdingLin/Re2A.

Problem

• situated conversational recommendation
• user preferences
• situation consistency

Innovation

• situated conversational recommendation
• rubric-based preference reasoning
• preference-conditioned optimization