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

arXiv:2609.18249 · cs.AI · Submitted 2026-09-16 · Read on arXiv

cs.AI

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: EMNLP 2026 MainConference

Code: https://github.com/DongdingLin/Re2A

License: http://creativecommons.org/licenses/by/4.0/

The gist: Real-world recommendation scenarios are commonly grounded in shared physical environments during user-recommender interactions.

Terminology

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.

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