One-shot Multi-view Visual Conversational Recommendation

We propose One-Shot Multi-view Visual Conversational Recommendation (OSMVR), a novel framework to tackle key challenges in personalized recommendation systems from minimal user input. OSMVR addresses three critical issues: the Cold Start problem, the lack of transparent justifications, and the difficulty of accurately estimating nuanced user preferences. Unlike multi-turn systems, OSMVR reduces user burden by structuring the recommendation process into two phases-Preference Estimation and Recommendation Generation-within a single conversational round. It leverages a multi-view approach that enables one-shot preference inference and multi-perspective recommendation generation.