@inproceedings{long-etal-2023-multimodal,
    title = "Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark",
    author = "Long, Yuxing  and
      Hui, Binyuan  and
      Yuan, Caixia  and
      Huang, Fei  and
      Li, Yongbin  and
      Wang, Xiaojie",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.217",
    doi = "10.18653/v1/2023.findings-acl.217",
    pages = "3515--3533",
    abstract = "Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario. This paper introduces a new dataset SURE (Multimodal Recommendation Dialog with Subjective Preference), which contains 12K shopping dialogs in complex store scenes. The data is built in two phases with human annotations to ensure quality and diversity. SURE is well-annotated with subjective preferences and recommendation acts proposed by sales experts. A comprehensive analysis is given to reveal the distinguishing features of SURE. Three benchmark tasks are then proposed on the data to evaluate the capability of multimodal recommendation agents. Basing on the SURE, we propose a baseline model, powered by a state-of-the-art multimodal model, for these tasks.",
}

Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario. This paper introduces a new dataset SURE (Multimodal Recommendation Dialog with Subjective Preference), which contains 12K shopping dialogs in complex store scenes. The data is built in two phases with human annotations to ensure quality and diversity. SURE is well-annotated with subjective preferences and recommendation acts proposed by sales experts. A comprehensive analysis is given to reveal the distinguishing features of SURE. Three benchmark tasks are then proposed on the data to evaluate the capability of multimodal recommendation agents. Basing on the SURE, we propose a baseline model, powered by a state-of-the-art multimodal model, for these tasks.
Long, Yuxing Hui, Binyuan Yuan, Caixia Huang, Fei Li, Yongbin Wang, Xiaojie
Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark