Minqi Sun

David Guo, Minqi Sun, Yilun Jiang et al. · utoronto

Multimodal conversational recommendation has recently emerged as a promising paradigm for delivering personalized experiences through natural dialogue enriched by visual and contextual grounding. Yet currently available multimodal conversational recommendation datasets remain limited: existing resources either simulate conversations, omit user history or fail to collect sufficiently detailed feedback, which constrain the types of research and evaluation they support. To address these gaps we introduce VOGUE, a dataset of 60 human human dialogues containing 2100 granularly labeled utterances in realistic fashion shopping scenarios. Each dialogue is paired with a shared visual catalogue, item metadata, user fashion profiles and post conversation ratings from both users (Seekers) and recommenders (Assistants). This design enables rigorous evaluation of conversational inference, including not only alignment between predicted and ground truth preferences but also calibration against full rating distributions and comparison with explicit and implicit user satisfaction signals. Our analyses of VOGUE reveal distinctive dynamics of visually grounded dialogue, e.g. recommenders frequently recommend items simultaneously in feature based groups, which creates distinct conversational phases bridged by Seeker critiques and refinements. Benchmarking Multimodal Large Language Models against human Recommenders shows that while MLLMs approach human level alignment in aggregate they exhibit systematic distribution errors in reproducing human ratings and struggle to generalize preference inference beyond explicitly discussed items. These findings establish VOGUE as both a unique resource for studying multimodal conversational systems and a challenge dataset beyond the current recommendation capabilities of existing top tier multimodal foundation models such as GPT-5-mini and Gemini-2.5-Flash.

Jiazhou Liang, Yifan Simon Liu, David Guo et al.

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, meta-data 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.