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    <abstract>The growing ubiquity of Extended Reality (XR) is driving Conversational Recommendation Systems (CRS) toward visually immersive experiences. We formalize this paradigm as Immersive CRS (ICRS), where recommended items are highlighted directly in the user’s scene-based visual environment and augmented with in-situ labels. While item recommendation has been widely studied, the problem of how to select and evaluate which information to present as immersive labels remains an open problem. To this end, we introduce a principled categorization of information needs into explicit intent satisfaction and proactive information needs and use these to define novel evaluation metrics for item label selection. We benchmark IR-, LLM-, and VLM-based methods across three datasets and ICRS scenarios: fashion, movie recommendation, and retail shopping. Our evaluation reveals three important limitations of existing methods: (1) they fail to leverage scenario-specific information modalities (e.g., visual cues for fashion, metadata for retail), (2) they present redundant information that is visually inferable, and (3) they poorly anticipate users’ proactive information needs from explicit dialogue alone. In summary, this work provides both a novel evaluation paradigm for in-situ item labeling in ICRS and highlights key challenges for future work.</abstract>
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%0 Conference Proceedings
%T Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation
%A Liang, Jiazhou
%A Liu, Yifan Simon
%A Guo, David
%A Jiang, Yilun
%A Sun, Minqi
%A Sanner, Scott
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liang-etal-2026-evaluating
%X The growing ubiquity of Extended Reality (XR) is driving Conversational Recommendation Systems (CRS) toward visually immersive experiences. We formalize this paradigm as Immersive CRS (ICRS), where recommended items are highlighted directly in the user’s scene-based visual environment and augmented with in-situ labels. While item recommendation has been widely studied, the problem of how to select and evaluate which information to present as immersive labels remains an open problem. To this end, we introduce a principled categorization of information needs into explicit intent satisfaction and proactive information needs and use these to define novel evaluation metrics for item label selection. We benchmark IR-, LLM-, and VLM-based methods across three datasets and ICRS scenarios: fashion, movie recommendation, and retail shopping. Our evaluation reveals three important limitations of existing methods: (1) they fail to leverage scenario-specific information modalities (e.g., visual cues for fashion, metadata for retail), (2) they present redundant information that is visually inferable, and (3) they poorly anticipate users’ proactive information needs from explicit dialogue alone. In summary, this work provides both a novel evaluation paradigm for in-situ item labeling in ICRS and highlights key challenges for future work.
%R 10.18653/v1/2026.findings-acl.2033
%U https://aclanthology.org/2026.findings-acl.2033/
%U https://doi.org/10.18653/v1/2026.findings-acl.2033
%P 40932-40953

Markdown (Informal)

[Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation](https://aclanthology.org/2026.findings-acl.2033/) (Liang et al., Findings 2026)

• Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation (Liang et al., Findings 2026)

ACL

• Jiazhou Liang, Yifan Simon Liu, David Guo, Yilun Jiang, Minqi Sun, and Scott Sanner. 2026. Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 40932–40953, San Diego, California, United States. Association for Computational Linguistics.