Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation

2026-04-06

AI Summary

This work addresses the open challenge of selecting and evaluating item attributes suitable for in-scene labeling within immersive conversational recommender systems. Focusing on extended reality environments, the study proposes a framework grounded in explicit intent satisfaction and proactive information needs. It first establishes a taxonomy of information needs tailored to immersive contexts, then introduces novel evaluation metrics specifically designed for in-scene labels. Leveraging techniques from information retrieval, large language models, and vision-language models, the authors conduct benchmark experiments across fashion, movie, and retail domains. The findings reveal critical limitations of current approaches in effectively integrating scene modalities, mitigating visual redundancy, and anticipating proactive user demands, thereby highlighting the proposed framework’s innovative contribution to enhancing label relevance and user experience.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Computer Vision: Scene Analysis & Understanding
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

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, 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.

Problem

• Immersive Conversational Recommendation
• In-Situ Labeling
• Information Needs
• Extended Reality
• Item Recommendation

Innovation

• Immersive Conversational Recommendation
• In-situ Labeling
• Information Needs Categorization
• Multimodal Evaluation
• Extended Reality