@inproceedings{
lin2024screen,
title={{SCREEN}: A Benchmark for Situated Conversational Recommendation},
author={Dongding Lin and Jian Wang and Chak Tou Leong and Wenjie Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=BfjHOCFvyf}
}
Engaging in conversational recommendations within a specific scenario represents a promising paradigm in the real world. Scenario-relevant situations often affect conversations and recommendations from two closely related aspects: varying the appealingness of items to users, namely $\textit{situated item representation}$, and shifting user interests in the targeted items, namely $\textit{situated user preference}$. We highlight that considering those situational factors is crucial, as this aligns with the realistic conversational recommendation process in the physical world. However, it is challenging yet under-explored. In this work, we are pioneering to bridge this gap and introduce a novel setting: $\textit{Situated Conversational Recommendation Systems}$ (SCRS). We observe an emergent need for high-quality datasets, and building one from scratch requires tremendous human effort. To this end, we construct a new benchmark, named $\textbf{SCREEN}$, via a role-playing method based on multimodal large language models. We take two multimodal large language models to play the roles of a user and a recommender, simulating their interactions in a co-observed scene. Our SCREEN comprises over 20k dialogues across 1.5k diverse situations, providing a rich foundation for exploring situational influences on conversational recommendations. Based on the SCREEN, we propose three worth-exploring subtasks and evaluate several representative baseline models. Our evaluations suggest that the benchmark is high quality, establishing a solid experimental basis for future research. The code and data are available at https://github.com/DongdingLin/SCREEN.
SCREEN: A Benchmark for Situated Conversational Recommendation
Dongding Lin Jian Wang Chak Tou Leong Wenjie Li