Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark
This addresses the problem of limited data for multimodal recommendation agents in shopping scenarios, but it is incremental as it focuses on dataset creation rather than novel methods.
The paper tackles the lack of datasets capturing user subjective preferences in multimodal recommendation dialogs by introducing SURE, a new dataset with 12K shopping dialogs annotated by experts, and proposes three benchmark tasks with a baseline model.
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. Based on the SURE, we propose a baseline model, powered by a state-of-the-art multimodal model, for these tasks.